From 4ee0ad87bbf81c90556488ac3e2f0b7969c1bec7 Mon Sep 17 00:00:00 2001 From: Furen Xiao Date: Wed, 29 Jul 2026 13:48:09 +0800 Subject: [PATCH] medgemma works --- .gitignore | 2 + 2026/export-all-patho.py | 16 ++ ntuh/new_window.png | Bin 91002 -> 0 bytes ntuh/old_window.png | Bin 34008 -> 0 bytes ntuh/screenshot.png | Bin 128972 -> 0 bytes patho/0727/extract.py | 435 ++++++++++++++++++++++++++++++++++++++ patho/0727/fetch.py | 64 ++++++ patho/extract-medgemma.py | 266 +++++++++++++++++++++++ patho/extract.py | 423 ++++++++++++++++++++++++++++++++++++ patho/fetch.py | 64 ++++++ 10 files changed, 1270 insertions(+) create mode 100644 2026/export-all-patho.py delete mode 100755 ntuh/new_window.png delete mode 100755 ntuh/old_window.png delete mode 100755 ntuh/screenshot.png create mode 100755 patho/0727/extract.py create mode 100755 patho/0727/fetch.py create mode 100644 patho/extract-medgemma.py create mode 100755 patho/extract.py create mode 100755 patho/fetch.py diff --git a/.gitignore b/.gitignore index c7d91ce..d0833e6 100644 --- a/.gitignore +++ b/.gitignore @@ -167,6 +167,8 @@ components .vscode/ whoosh_index/ +*.csv +*.log *.pdf *.seg *.sql diff --git a/2026/export-all-patho.py b/2026/export-all-patho.py new file mode 100644 index 0000000..7b2bafb --- /dev/null +++ b/2026/export-all-patho.py @@ -0,0 +1,16 @@ +import pandas as pd +import pymysql +import os + +# Configure database connection +conn = pymysql.connect(host='localhost', user='ntuh', password='n122119493', db='adm15') + +# Execute query and load into a pandas DataFrame +df = pd.read_sql_query("SELECT * FROM registry_pathologyreport", conn) + +# Export DataFrame to an Excel file +# output_file = '/data/patho-2026.xlsx' +# df.to_excel(output_file, index=False) +output_file_parquet = '/data/patho-2026.parquet' +df.to_parquet(output_file_parquet, index=False, engine='pyarrow') +print(f"Data exported to {output_file_parquet}") diff --git a/ntuh/new_window.png b/ntuh/new_window.png deleted file mode 100755 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+script_dir = os.path.dirname(os.path.abspath(__file__)) +base_dir = os.path.dirname(script_dir) + + +def is_valid_tissue(t): + if not t or not isinstance(t, str): + return False + t = t.strip() + clean_t = re.sub(r'[\.,\-\_\s\(\)\"\']', '', t) + clean_no_dim = re.sub(r'\d+[\.\*x\d]*cm', '', clean_t, flags=re.IGNORECASE) + if not clean_t or len(clean_t) <= 2 or clean_t.lower() in ['none', 'nil']: + return False + if clean_t.upper() in ['BM', 'BX', 'CX', 'HX', 'PX', 'DX', 'NA', 'NB']: + return False + tumor_terms = ['thrombus', 'clot', 'hematoma', 'abscess', 'carcinoma', 'adenocarcinoma', 'adenoma', 'polyp', 'cyst', 'synovialcyst', 'tumor', 'tumour', 'mass', 'nodule', 'glioma', 'astrocytoma', 'meningioma', 'schwannoma', 'lymphoma', 'melanoma'] + if clean_t.lower() in tumor_terms or any(clean_t.lower().endswith(k) for k in ['cyst', 'tumor', 'tumour', 'glioma']) or any(clean_no_dim.lower().endswith(k) for k in ['cyst', 'tumor', 'tumour', 'glioma']) or 'subcutaneoustumor' in clean_t.lower() or 'intraduraltumor' in clean_t.lower() or 'hyperintensity' in clean_t.lower() or 'hypointensity' in clean_t.lower() or 'duraseeding' in clean_t.lower() or 'cavitytumor' in clean_t.lower(): + return False + if '位置不明' in t or 'sitenotstate' in clean_t.lower() or 'siteunspecified' in clean_t.lower(): + return False + if any(k in clean_t.lower() for k in ['recurrenttumor', 'granulationtis', 'suspecttumor', 'rotumor', 'ronecros']): + return False + invalid_keywords = ['送檢單位', '切取日期', '固定日期', '檢查項目', '病理報告', '病歷號', '版本：', 'Result:', 'Comments:'] + if any(kw in t for kw in invalid_keywords): + return False + if any(t.startswith(prefix) for prefix in ['送檢', '切取', '檢查', '病理', '病歷', '版本']): + return False + return True + + +def extract_tissue(html_content, path_code=''): + if not html_content or not isinstance(html_content, str): + return "" + + soup = BeautifulSoup(html_content, 'html.parser') + + tissue = "" + # 1. Check lblTissue span + span = soup.find('span', id=lambda x: x and 'lblTissue' in x) + if span and is_valid_tissue(span.text): + tissue = span.text.strip() + + # 2. Check table row with 組織由來 + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + if '組織由來' in ths: + idx = ths.index('組織由來') + if idx < len(tds) and is_valid_tissue(tds[idx]): + tissue = tds[idx] + break + elif '組織由來' in tr.text: + if tds and is_valid_tissue(tds[-1]): + tissue = tds[-1] + + # 3. Check 器官與術式 / 器官 / 部位 / 檢體部位 + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + for header in ['器官與術式', '器官', '部位', '檢體部位']: + if header in ths: + idx = ths.index(header) + if idx < len(tds) and is_valid_tissue(tds[idx]): + tissue = tds[idx] + break + if tissue: + break + + # 4. Dig site from 組織學診斷 row if tissue is still empty + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + if any('組織學診斷' in th for th in ths): + if tds: + val = tds[-1] + if val: + s = re.sub(r'^\d+[\.\s]*', '', val) + split_site = re.split(r',\s*(?:\d+(?:\-\d+)?\s*cm(?:\s+AAV|\s+above\s+anal\s+verge|\s+[A-Za-z]+)?|AAV|above\s+anal\s+verge|tumor\s+resection|tumor\s+excision|metastasis\s+excision|effusion\s+drainage|drainage|reconstruction|debridement|spondylectomy|laminectomy|discectomy|arthroplasty|fixation|fusion|stripping|incisional(?:\s+biopsy)?|excisional(?:\s+biopsy)?|punch(?:\s+biopsy)?|shave(?:\s+biopsy)?|wedge(?:\s+biopsy)?|core(?:\s+biopsy)?|site\s+not\s+state(?:d)?|site\s+unspecified|\?\s*side|\?|craniotomy(?:\s+and\s+|\s*\&\s*|\s+)excision|craniotomy|FESS|transphenoid|transsphenoidal|panendoscopic|choledochoscopic|colonoscopic|endoscopical|endoscopic|laparoscopic|transcervical|open|biopsy|polypectomy|(?:partial|subtotal|radical|wide|total)?\s*resection|excision|curettage|aspiration|dissection|cholecystectomy|hysterectomy|nephrectomy|lobectomy|mastectomy|meningioma|lipoma|fibroma|adenoma|carcinoma|polyp|hyperplasia|cyst|gastritis|sinusitis|cholecystectomy|fibrosis)\b', s, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*(?:corpus|cervix)\b', split_site, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site) and split_site != val: + tissue = split_site.strip() + break + + # 5. Check active report tab link: + if not tissue: + a_tag = soup.find('a', href=lambda x: x and 'contentHolder' in x, title=True) + if a_tag and is_valid_tissue(a_tag.get('title')): + tissue = a_tag['title'].strip() + + # 6. Check selected option: + if not tissue: + opt = soup.find('option', selected=True) + if opt and opt.text: + m = re.search(r'\(([^()]+)\)$', opt.text) + if m and is_valid_tissue(m.group(1)): + tissue = m.group(1).strip() + + # 7. Dig from 檢查報告 (lblResult or textarea or full text) if still blank/dot + if not tissue: + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.get_text(separator='\n') if ta else (res_span.get_text(separator='\n') if res_span else soup.get_text(separator='\n'))).strip() + if raw_text: + m = re.search(r'(?:Pathology Number of specimen|Pathology Number|Specimen Number|sample number|specimen number|from sample|from specimen|sample|specimen)\s*[:：]\s*([A-Za-z0-9_\-]+)|(?:from sample|from specimen|sample number|specimen number)\s*[:：\s]\s*([A-Za-z0-9_\-]+)', raw_text, re.IGNORECASE) + matched_code = (m.group(1) or m.group(2)).strip() if m else None + if matched_code: + tissue = matched_code + else: + lines = [l.strip() for l in raw_text.splitlines() if l.strip()] + if lines: + first_line = lines[0] + if not any(k in first_line for k in ['MACROSCOPIC', 'MICROSCOPIC', 'The specimen', 'Grossly']): + split_site = re.split(r',\s*(?:\d+(?:\-\d+)?\s*cm(?:\s+AAV|\s+above\s+anal\s+verge|\s+[A-Za-z]+)?|AAV|above\s+anal\s+verge)\b', first_line, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*(?:[A-Za-z\s]+)?(?:tumor\s+resection|tumor\s+excision|spondylectomy|laminectomy|discectomy|thrombus|clot|hematoma|abscess|echo-guided|ct-guided|us-guided|ultrasound-guided|incisional|excisional|punch|shave|wedge|core|needle|fine\s+needle|colonoscopic|endoscopic|laparoscopic|transcervical|open)?\s*(?:biopsy|aspiration|fna|polypectomy|resection|excision|curettage|dissection|reconstruction|debridement|drainage|tumor\s+resection|tumor\s+excision|spondylectomy|laminectomy|discectomy|tubular adenoma|adenocarcinoma|carcinoma|polyp|abscess|thrombus|clot|hematoma|necrosis)\b[^\n\r,]*', first_line, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*labeled\s+as\b', split_site, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site): + tissue = split_site.strip() + + # If tissue is LN or a reference code like S2555571, expand or check description + if tissue.upper() == 'LN': + tissue = 'Lymph node' + + if not tissue: + span = soup.find('span', id=lambda x: x and 'lblTissue' in x) + if span and span.text.strip().upper() == 'LN': + tissue = 'Lymph node' + + if tissue and re.match(r'^[A-Za-z0-9_\-]+$', tissue): + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.get_text(separator='\n') if ta else (res_span.get_text(separator='\n') if res_span else soup.get_text(separator='\n'))).strip() + if raw_text: + m = re.search(rf'{re.escape(tissue)}\s+([A-Za-z][^\n\r]+)', raw_text) + if m: + desc = m.group(1).strip() + split_site = re.split(r',\s*(?:tumor\s+resection|tumor\s+excision|excision|resection|biopsy|carcinoma|adenoma|metastatic)\b', desc, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site) and split_site != desc and not re.match(r'^[A-Za-z0-9_\-]+$', split_site): + tissue = split_site.strip() + + return tissue + + +def extract_short_diagnosis(html_content, path_code=''): + if not html_content or not isinstance(html_content, str): + return "" + + if str(path_code).strip() in ['S2605800', 'S2602629'] or any(code in str(html_content) for code in ['S2605800', 'S2602629']): + soup = BeautifulSoup(html_content, 'html.parser') + ta = soup.find('textarea') + if ta: + lines = [l.strip() for l in ta.text.splitlines() if l.strip()] + if lines: + return lines[0] + + soup = BeautifulSoup(html_content, 'html.parser') + + # 1. Check table rows for 組織學診斷 / 診斷描述 + table_diags = [] + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + for h in ths: + if any(k in h for k in ['組織學診斷', '診斷描述']): + idx = ths.index(h) + if idx < len(tds): + val = tds[idx].text.strip() if hasattr(tds[idx], 'text') else str(tds[idx]).strip() + if val and val not in table_diags: + table_diags.append(val) + + if not table_diags: + for tr in soup.find_all('tr'): + cells = [td.text.strip() for td in tr.find_all(['th', 'td'])] + text = ' '.join(cells) + if any(k in text for k in ['組織學診斷', '診斷描述']): + tds = tr.find_all('td') + if tds: + val = tds[-1].text.strip() + if val and val not in table_diags: + table_diags.append(val) + + if table_diags: + return ', '.join(table_diags) + + # 2. Check textarea or lblResult + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.text if ta else (res_span.text if res_span else '')).strip() + + if raw_text: + # Check explicit Histological diagnosis field in text + m_histo = re.search(r'Histological diagnosis[:：\s]\s*([^\*\n\r]+)', raw_text, re.IGNORECASE) + if m_histo and m_histo.group(1).strip(): + return m_histo.group(1).strip() + + # Special handling for Molecular / Special laboratory reports starting with 送檢單位 or 檢查項目 + if '送檢單位' in raw_text or '檢查項目' in raw_text: + if '檢查項目:' in raw_text or '檢查項目：' in raw_text: + m = re.search(r'檢查項目[:：]\s*(.*?)(?:Pathology Number|Probe:|Control:|Duration|Result:|檢驗報告|送檢|\n|\r|\s{4,}|$)', raw_text, re.DOTALL) + if m and m.group(1).strip(): + item = m.group(1).strip() + m_res = re.search(r'(?:Result:|檢驗報告[:：])\s*(.*?)(?:Procedures:|Comments:|參考文獻|Ref:|\n\n|\r\n\r\n|\s{4,}|$)', raw_text, re.DOTALL) + if m_res and m_res.group(1).strip(): + res_val = m_res.group(1).strip().replace('\n', ' ').replace('\t', ' ') + res_val = re.sub(r'\s{2,}', ' ', res_val) + return f'{item}; Result: {res_val}' + return item + elif '檢驗報告' in raw_text or 'Result:' in raw_text: + m_res = re.search(r'(?:Result:|檢驗報告[:：])\s*(.*?)(?:Procedures:|Comments:|參考文獻|Ref:|\n\n|\r\n\r\n|\s{4,}|$)', raw_text, re.DOTALL) + if m_res and m_res.group(1).strip(): + res_val = m_res.group(1).strip().replace('\n', ' ').replace('\t', ' ') + return re.sub(r'\s{2,}', ' ', res_val) + + # Standard Pathology Report + cut_patterns = [ + r'The specimen submitted', r'Grossly', r'Microscopically', r'Pathology Number', + r'送檢單位', r'檢查項目', r'Result:', r'Comments:', r'Ref:', r'\[\d+\]\.' + ] + text_cut = re.split('|'.join(cut_patterns), raw_text, flags=re.IGNORECASE)[0] + parts = re.split(r'\n|\r|\t|\s{2,}', text_cut) + non_empty = [p.strip() for p in parts if p.strip()] + if non_empty: + first_p = non_empty[0] + m_end = re.search(r',\s*([A-Z][A-Za-z\s\-]+(?:adenoma|carcinoma|polyp|hyperplasia|gastritis|inflammation|fibrosis|cyst|lipoma|lymphoma))$', first_p, re.IGNORECASE) + if m_end: + return m_end.group(1).strip() + return first_p + + # 3. Fallback to lblBedDiagnosis if available + bed_span = soup.find('span', id=lambda x: x and 'lblBedDiagnosis' in x) + if bed_span and bed_span.text.strip(): + return bed_span.text.strip() + + return "" + + +def classify_site(tissue, diagnosis, html=''): + site_text = (str(tissue) + ' ' + str(diagnosis)).lower() + site_text = re.sub(r'\bpd\-?l1\b', '', site_text, flags=re.IGNORECASE) + + spinal_kw = [ + 'spine', 'spinal', 'vertebra', 'vertebrae', 'vertebral', 'lumbar', 'thoracic', 'cervical', + 'sacral', 'sacrum', 'epidural', 'paraspinal', 'disc', 'spondylectomy', 'laminectomy', 'discectomy', + 'ligamentum flavum', 'ligamentum', 'ulbd', 'neural placode', 'placode' + ] + spinal_patterns = [r'\b' + k + r'\b' for k in spinal_kw] + [r'\b[ltcs]\d+(?:[\-\/]\d+)?\b'] + + cranial_kw = [ + 'brain', 'brainstem', 'cerebrum', 'cerebral', 'cerebellar', 'cerebellum', 'pituitary', 'cranial', 'cranium', + 'skull', 'dura', 'dural', 'temporal', 'frontal', 'parietal', 'occipital', 'ventricle', + 'paranasal', 'head', 'scalp', 'orbit', 'sellar', 'suprasellar', 'cerebellopontine', 'cp angle', 'cpangle', + 'meninx', 'meninges', 'sphenoid', 'sinonasal', 'nasal cavity', 'meningioma', 'forehead' + ] + cranial_patterns = [r'\b' + k + r'\b' for k in cranial_kw] + [r'\bcp\s+angle\b', r'\bnasal\s+cavity\b'] + + is_spinal = any(re.search(p, site_text) for p in spinal_patterns) + is_cranial = any(re.search(p, site_text) for p in cranial_patterns) + + if 'dura mater' in site_text or 'dural' in site_text and 'epidural' not in site_text: + is_cranial = True + + if is_spinal and not is_cranial: + return 'spinal' + elif is_cranial and not is_spinal: + return 'cranial' + elif is_spinal and is_cranial: + if any(k in site_text for k in ['brain', 'cerebrum', 'dura mater']): + return 'cranial' + return 'spinal' + + return 'other' + + +def classify_type(tissue, diagnosis, html=''): + diag_str = str(diagnosis).lower() + text = (str(tissue) + ' ' + diag_str).lower() + + if any(k in diag_str for k in ['no evidence of', 'negative for', 'free of', 'no tumor', 'without tumor', 'no residual']): + return 'other' + + if any(k in diag_str for k in ['necrotic tissue', 'necrosis', 'organizing hematoma', 'hematoma', 'thrombus', 'clot', 'abscess', 'infarction', 'fibrosis', 'sinusitis', 'cholecystitis', 'gastritis', 'varicose']): + definitive_tumor = ['carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', 'osteoma', 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'subependymoma', 'ependymoma', 'lipoma', 'fibroma', 'papilloma', 'lymphoma', 'melanoma', 'schwannoma', 'malignancy', 'malignant'] + if not any(k in diag_str for k in definitive_tumor): + return 'other' + + non_tumor_findings = ['granuloma', 'inflammatory cell infiltration', 'organizing hematoma', 'hematoma', 'thrombus', 'clot', 'abscess', 'necrosis', 'infarction', 'fibrosis', 'sinusitis', 'cholecystitis', 'gastritis', 'varicose', 'amyloid angiopathy'] + neoplastic_findings = [ + 'carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', 'osteoma', 'chondroma', 'hemangioma', 'neuroma', 'leiomyoma', 'myxoma', + 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'subependymoma', 'ependymoma', 'ganglioglioma', 'oligodendroglioma', 'medulloblastoma', 'chordoma', 'hemangioblastoma', 'craniopharyngioma', 'lipoma', 'fibroma', 'papilloma', + 'polyp', 'hyperplasia', 'metastasis', 'metastatic', 'malignancy', 'malignant', + 'schwannoma', 'neurofibroma', 'melanoma', 'lymphoma', 'plasmacytosis', 'neoplastic', 'who grade', 'cns who grade' + ] + + if any(k in diag_str for k in non_tumor_findings) and not any(k in diag_str for k in neoplastic_findings): + return 'other' + + tumor_kw = [ + 'malignancy', 'malignant', 'osteoma', 'chondroma', 'hemangioma', 'neuroma', 'leiomyoma', 'myxoma', + 'subependymoma', 'ependymoma', 'ganglioglioma', 'oligodendroglioma', 'medulloblastoma', 'chordoma', 'hemangioblastoma', 'craniopharyngioma', 'who grade', 'cns who grade', 'tumor', 'tumour', 'carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', + 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'lipoma', 'fibroma', 'papilloma', 'cyst', + 'polyp', 'hyperplasia', 'mass', 'lesion', 'neoplasm', 'metastasis', 'metastatic', + 'schwannoma', 'neurofibroma', 'melanoma', 'lymphoma', 'plasmacytosis', 'neoplastic' + ] + + if any(k in text for k in tumor_kw): + return 'tumor' + + return 'other' + + +def process_extraction(input_excel=None, output_csv=None, limit=None, mode='all'): + if input_excel is None: + input_excel = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + if not os.path.exists(input_excel) and os.path.exists('registry_pathologyreport.xlsx'): + input_excel = 'registry_pathologyreport.xlsx' + + if output_csv is None: + output_csv = os.path.join(script_dir, 'registry_pathologyreport_extracted.csv') + + print(f"Opening Excel file: {input_excel} ...") + wb = openpyxl.load_workbook(input_excel, read_only=True) + ws = wb.active + + headers = None + chart_idx = None + path_idx = None + html_idx = None + + all_raw_rows = [] + + print(f"Reading Excel rows...") + for i, row in enumerate(ws.iter_rows()): + vals = [cell.value for cell in row] + if headers is None: + headers = vals + chart_idx = headers.index('ChartNo') + path_idx = headers.index('PathCode') + html_idx = headers.index('html') + continue + + all_raw_rows.append(vals) + + if mode == 'last' and limit: + raw_rows = all_raw_rows[-limit:] + print(f"Processing last {len(raw_rows)} rows (out of {len(all_raw_rows)} total)...") + else: + raw_rows = all_raw_rows[:limit] if limit else all_raw_rows + print(f"Processing {len(raw_rows)} rows...") + + # Build full PathCode map from all Excel rows for fallback resolution + excel_pathcode_html_map = {} + for vals in all_raw_rows: + pcode = str(vals[path_idx]).strip() if vals[path_idx] else "" + hval = vals[html_idx] or "" + if pcode: + excel_pathcode_html_map[pcode] = hval + + rows = [] + pathcode_diag_map = {} + + for vals in raw_rows: + chart_no = vals[chart_idx] + path_code = str(vals[path_idx]).strip() if vals[path_idx] else "" + html_content = vals[html_idx] or "" + + tissue = extract_tissue(html_content, path_code) + diagnosis = extract_short_diagnosis(html_content, path_code) + + site = classify_site(tissue, diagnosis, html_content) + ttype = classify_type(tissue, diagnosis, html_content) + + row_dict = { + 'chart_no': chart_no, + 'path_code': path_code, + 'site': site, + 'type': ttype, + 'tissue': tissue, + 'diagnosis': diagnosis + } + rows.append(row_dict) + + if path_code and diagnosis: + pathcode_diag_map[path_code] = diagnosis + + print("Resolving empty diagnosis rows via 組織由來 (tissue) -> 病理號 (PathCode) matching...") + fallback_count = 0 + for r in rows: + if not r['diagnosis'] and r['tissue']: + tissue = r['tissue'].strip() + potential_codes = [tissue] + re.findall(r'[A-Za-z0-9_\-]+', tissue) + for code in potential_codes: + if code not in pathcode_diag_map and code in excel_pathcode_html_map: + ref_html = excel_pathcode_html_map[code] + if ref_html: + db_diag = extract_short_diagnosis(ref_html, code) + if db_diag: + pathcode_diag_map[code] = db_diag + + if code in pathcode_diag_map and pathcode_diag_map[code]: + r['diagnosis'] = pathcode_diag_map[code] + r['site'] = classify_site(r['tissue'], r['diagnosis']) + r['type'] = classify_type(r['tissue'], r['diagnosis']) + fallback_count += 1 + break + + print(f"Fallback resolution complete: updated {fallback_count} empty rows.") + print(f"Writing to CSV file: {output_csv} ...") + + with open(output_csv, 'w', newline='', encoding='utf-8-sig') as f: + writer = csv.writer(f) + writer.writerow(['病歷號', '病理號', 'site', 'type', '組織由來', 'pathological diagnosis']) + + for r in rows: + writer.writerow([r['chart_no'], r['path_code'], r['site'], r['type'], r['tissue'], r['diagnosis']]) + + print(f"Successfully exported {len(rows)} rows to {output_csv}") + + +if __name__ == '__main__': + inp = sys.argv[1] if len(sys.argv) > 1 else None + out = sys.argv[2] if len(sys.argv) > 2 else None + lim = int(sys.argv[3]) if len(sys.argv) > 3 and sys.argv[3].isdigit() else None + m = sys.argv[4] if len(sys.argv) > 4 else 'all' + process_extraction(inp, out, limit=lim, mode=m) diff --git a/patho/0727/fetch.py b/patho/0727/fetch.py new file mode 100755 index 0000000..f21729c --- /dev/null +++ b/patho/0727/fetch.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import os +import sys +import re + +# Add repository root and 'ntuh' directory to sys.path +script_dir = os.path.dirname(os.path.abspath(__file__)) +base_dir = os.path.dirname(script_dir) +ntuh_dir = os.path.join(base_dir, 'ntuh') + +if base_dir not in sys.path: + sys.path.insert(0, base_dir) +if ntuh_dir not in sys.path: + sys.path.insert(0, ntuh_dir) + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ntuh.settings') + +import django +django.setup() + +import pandas as pd +from registry.models import PathologyReport + +illegal_re = re.compile(r'[\x00-\x08\x0b-\x0c\x0e-\x1f]') + + +def clean_string_for_excel(val): + if not isinstance(val, str): + return val + # Remove illegal XML / openpyxl control characters + val = illegal_re.sub('', val) + # Strip unnecessary head, style, script, meta, link tags so report body fits within cell limits + h = re.sub(r'<(?:head|style|script|meta|link)[^>]*?>.*?', '', val, flags=re.DOTALL | re.IGNORECASE) + h = re.sub(r'<(?:meta|link)[^>]*?>', '', h, flags=re.IGNORECASE) + h = re.sub(r'\n\s*\n', '\n', h) + return h.strip() + + +def fetch_pathology_reports(output_file=None): + if output_file is None: + output_file = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + + print(f"Fetching PathologyReport records from database...") + qs = PathologyReport.objects.all().values() + data = list(qs) + + print(f"Cleaning string fields & HTML body for Excel cell limit...") + for item in data: + for k, v in item.items(): + if isinstance(v, str): + item[k] = clean_string_for_excel(v) + + df = pd.DataFrame(data) + print(f"Fetched {len(df)} rows. Writing to {output_file}...") + df.to_excel(output_file, index=False) + print(f"Successfully exported {len(df)} rows to {output_file}") + + +if __name__ == '__main__': + default_output = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + target_path = sys.argv[1] if len(sys.argv) > 1 else default_output + fetch_pathology_reports(target_path) diff --git a/patho/extract-medgemma.py b/patho/extract-medgemma.py new file mode 100644 index 0000000..ba9ae8c --- /dev/null +++ b/patho/extract-medgemma.py @@ -0,0 +1,266 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import os +import sys +import csv +import re +import json +import time +import datetime +import urllib.request +import urllib.parse +import openpyxl +from bs4 import BeautifulSoup +from concurrent.futures import ThreadPoolExecutor, as_completed + +script_dir = os.path.dirname(os.path.abspath(__file__)) +base_dir = os.path.dirname(script_dir) + +OLLAMA_URL = "http://192.168.221.20:11434/api/generate" +MODEL_NAME = "medgemma:27b" +LOG_FILE_PATH = os.path.join(script_dir, "ollama_calls.log") + + +def log_ollama_call(path_code, attempt, duration_sec, status, site="", ttype="", tissue="", diagnosis="", error=None): + timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + file_exists = os.path.exists(LOG_FILE_PATH) and os.path.getsize(LOG_FILE_PATH) > 0 + + try: + with open(LOG_FILE_PATH, "a", newline="", encoding="utf-8-sig") as f: + writer = csv.writer(f) + if not file_exists: + writer.writerow(["timestamp", "path_code", "attempt", "duration_sec", "status", "site", "type", "tissue", "diagnosis", "error"]) + + writer.writerow([ + timestamp, + path_code, + attempt, + round(duration_sec, 3), + status, + site, + ttype, + tissue, + diagnosis, + str(error) if error else "" + ]) + except Exception as e: + pass + + +def extract_plain_text(html_content): + if not html_content or not isinstance(html_content, str): + return "" + soup = BeautifulSoup(html_content, 'html.parser') + + # Remove script and style tags + for s in soup(['script', 'style', 'head', 'meta', 'link']): + s.decompose() + + text = soup.get_text(separator=' ').strip() + return re.sub(r'\s+', ' ', text)[:3000] + + +def enforce_site_rules(site, tissue, diagnosis): + tissue_lower = str(tissue).lower() + + # Rule: If tissue is Nasopharynx or Nasal cavity, site is NOT cranial (should be other). + # However, sinonasal IS cranial. + if 'sinonasal' in tissue_lower: + site = 'cranial' + elif 'nasopharynx' in tissue_lower or 'nasal cavity' in tissue_lower: + if site == 'cranial': + site = 'other' + + return site + + +def extract_with_medgemma_pure(html_content, path_code='', retries=5): + text_content = extract_plain_text(html_content) + + prompt = f"""You are an expert medical pathology analysis assistant. +Analyze the following pathology report text for specimen {path_code} and extract key clinical information. + +Report Text: +{text_content} + +Tasks: +1. "site": Anatomical site classification. Must be EXACTLY one of: "cranial", "spinal", or "other". + - "cranial": Brain, cerebrum, cerebellum, brainstem, skull, cranial dura, pituitary, CP angle, orbit, sinonasal tissue, head, scalp, etc. + - "NOTE on Nasal cavity / Nasopharynx": If tissue origin is "Nasopharynx" or "Nasal cavity" alone, it is NOT cranial (classify as "other"). However, "sinonasal" IS cranial. + - "spinal": Spine, spinal cord, vertebra, lumbar/thoracic/cervical/sacral level, epidural, disc, ligamentum flavum, etc. + - "other": All other body organs or unspecified sites. + +2. "type": Pathological nature. Must be EXACTLY one of: "tumor" or "other". + - "tumor": Neoplasms, carcinomas, adenomas, gliomas, meningiomas, schwannomas, metastases, lipomas, polyps, cysts, or neoplastic findings. + - "other": Non-neoplastic findings (e.g. inflammation, hematoma, abscess, necrosis, normal tissue, thrombus). + +3. "tissue": Tissue origin / specimen site string in English (e.g., "Brain, cerebrum", "Soft tissue, L3", "Spine, L4-L5 level", "Bone, T12 vertebra", "Spinal cord, T7-T9"). + - CRITICAL: Always keep and preserve the specific spine level if specified in the report (e.g. "L3", "L4/5", "T7-9", "C4-5", "T12", "S1-S2", "C1-C2"). Do not strip or omit spine levels! + +4. "diagnosis": Concise histological / pathological diagnosis statement in English. + +Return ONLY a valid JSON object matching this schema: +{{ + "site": "cranial|spinal|other", + "type": "tumor|other", + "tissue": "tissue origin (including exact spine level if specified)", + "diagnosis": "pathological diagnosis" +}} +""" + + payload = { + "model": MODEL_NAME, + "prompt": prompt, + "stream": False, + "format": "json" + } + + for attempt in range(1, retries + 1): + start_time = time.time() + try: + req = urllib.request.Request( + OLLAMA_URL, + data=json.dumps(payload).encode('utf-8'), + headers={'Content-Type': 'application/json'} + ) + with urllib.request.urlopen(req, timeout=90) as resp: + elapsed = time.time() - start_time + res_data = json.loads(resp.read().decode('utf-8')) + resp_text = res_data.get('response', '') + parsed = json.loads(resp_text) + + site = str(parsed.get('site', '')).strip().lower() + ttype = str(parsed.get('type', '')).strip().lower() + tissue = str(parsed.get('tissue', '')).strip() + diagnosis = str(parsed.get('diagnosis', '')).strip() + + if site not in ['cranial', 'spinal', 'other']: + site = 'other' + if ttype not in ['tumor', 'other']: + ttype = 'other' + + site = enforce_site_rules(site, tissue, diagnosis) + + log_ollama_call( + path_code=path_code, + attempt=attempt, + duration_sec=elapsed, + status="SUCCESS", + site=site, + ttype=ttype, + tissue=tissue, + diagnosis=diagnosis + ) + + return site, ttype, tissue, diagnosis + + except Exception as e: + elapsed = time.time() - start_time + print(f"Warning: MedGemma API attempt {attempt}/{retries} error for {path_code}: {e}") + log_ollama_call( + path_code=path_code, + attempt=attempt, + duration_sec=elapsed, + status="ERROR", + error=e + ) + if attempt < retries: + time.sleep(3 * attempt) + + return "other", "other", "", "" + + +def process_single_row(args): + idx, chart_no, path_code, html_content, total_count = args + print(f"[{idx+1}/{total_count}] Extracting PathCode {path_code} with MedGemma...") + site, ttype, tissue, diagnosis = extract_with_medgemma_pure(html_content, path_code) + print(f" -> [{idx+1}/{total_count}] {path_code}: site={site}, type={ttype}, tissue={tissue!r}, diag={diagnosis!r}") + return idx, [chart_no, path_code, site, ttype, tissue, diagnosis] + + +def process_extraction_medgemma(input_excel=None, output_csv=None, limit=0, prefix_filter=None, max_workers=4): + if input_excel is None: + input_excel = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + if not os.path.exists(input_excel) and os.path.exists('registry_pathologyreport.xlsx'): + input_excel = 'registry_pathologyreport.xlsx' + + if output_csv is None: + if prefix_filter: + output_csv = os.path.join(script_dir, f'registry_pathologyreport_medgemma_{prefix_filter}.csv') + else: + output_csv = os.path.join(script_dir, 'registry_pathologyreport_medgemma_all.csv') + + print(f"Opening Excel file: {input_excel} ...") + wb = openpyxl.load_workbook(input_excel, read_only=True) + ws = wb.active + + headers = None + chart_idx = None + path_idx = None + html_idx = None + + raw_rows = [] + print(f"Reading Excel rows (prefix filter: {prefix_filter!r}, limit: {limit})...") + for i, row in enumerate(ws.iter_rows()): + vals = [cell.value for cell in row] + if headers is None: + headers = vals + chart_idx = headers.index('ChartNo') + path_idx = headers.index('PathCode') + html_idx = headers.index('html') + continue + + path_code = str(vals[path_idx]).strip() if vals[path_idx] else "" + if prefix_filter: + if not path_code.startswith(prefix_filter): + continue + + raw_rows.append(vals) + if limit and len(raw_rows) >= limit: + break + + total_count = len(raw_rows) + print(f"Total rows to extract: {total_count} (max_workers: {max_workers})...") + + tasks_args = [ + (idx, vals[chart_idx], str(vals[path_idx]).strip() if vals[path_idx] else "", vals[html_idx] or "", total_count) + for idx, vals in enumerate(raw_rows) + ] + + results_dict = {} + completed_count = 0 + + # Write CSV header initially + with open(output_csv, 'w', newline='', encoding='utf-8-sig') as f: + writer = csv.writer(f) + writer.writerow(['病歷號', '病理號', 'site', 'type', '組織由來', 'pathological diagnosis']) + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_idx = {executor.submit(process_single_row, arg): arg[0] for arg in tasks_args} + + for future in as_completed(future_to_idx): + idx, res_row = future.result() + results_dict[idx] = res_row + completed_count += 1 + + # Periodically dump progress every 50 rows + if completed_count % 50 == 0 or completed_count == total_count: + print(f"Progress: {completed_count}/{total_count} rows completed ({completed_count/total_count*100:.2f}%)...") + + print(f"Writing all {total_count} rows in original order to CSV: {output_csv} ...") + with open(output_csv, 'w', newline='', encoding='utf-8-sig') as f: + writer = csv.writer(f) + writer.writerow(['病歷號', '病理號', 'site', 'type', '組織由來', 'pathological diagnosis']) + for idx in range(total_count): + writer.writerow(results_dict[idx]) + + print(f"Successfully exported {total_count} rows to {output_csv}") + + +if __name__ == '__main__': + lim = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isdigit() else 0 + pref = sys.argv[2] if len(sys.argv) > 2 and sys.argv[2] != 'None' else None + out = sys.argv[3] if len(sys.argv) > 3 else None + workers = int(sys.argv[4]) if len(sys.argv) > 4 and sys.argv[4].isdigit() else 4 + process_extraction_medgemma(limit=lim, prefix_filter=pref, output_csv=out, max_workers=workers) diff --git a/patho/extract.py b/patho/extract.py new file mode 100755 index 0000000..cc4fa53 --- /dev/null +++ b/patho/extract.py @@ -0,0 +1,423 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import os +import sys +import csv +import re +import openpyxl +from bs4 import BeautifulSoup + +script_dir = os.path.dirname(os.path.abspath(__file__)) +base_dir = os.path.dirname(script_dir) + + +def is_valid_tissue(t): + if not t or not isinstance(t, str): + return False + t = t.strip() + clean_t = re.sub(r'[\.,\-\_\s\(\)\"\']', '', t) + clean_no_dim = re.sub(r'\d+[\.\*x\d]*cm', '', clean_t, flags=re.IGNORECASE) + if not clean_t or len(clean_t) <= 2 or clean_t.lower() in ['none', 'nil']: + return False + if clean_t.upper() in ['BM', 'BX', 'CX', 'HX', 'PX', 'DX', 'NA', 'NB']: + return False + tumor_terms = ['thrombus', 'clot', 'hematoma', 'abscess', 'carcinoma', 'adenocarcinoma', 'adenoma', 'polyp', 'cyst', 'synovialcyst', 'tumor', 'tumour', 'mass', 'nodule', 'glioma', 'astrocytoma', 'meningioma', 'schwannoma', 'lymphoma', 'melanoma'] + if clean_t.lower() in tumor_terms or any(clean_t.lower().endswith(k) for k in ['cyst', 'tumor', 'tumour', 'glioma']) or any(clean_no_dim.lower().endswith(k) for k in ['cyst', 'tumor', 'tumour', 'glioma']) or 'subcutaneoustumor' in clean_t.lower() or 'intraduraltumor' in clean_t.lower() or 'hyperintensity' in clean_t.lower() or 'hypointensity' in clean_t.lower() or 'duraseeding' in clean_t.lower() or 'cavitytumor' in clean_t.lower(): + return False + if '位置不明' in t or 'sitenotstate' in clean_t.lower() or 'siteunspecified' in clean_t.lower(): + return False + if any(k in clean_t.lower() for k in ['recurrenttumor', 'granulationtis', 'suspecttumor', 'rotumor', 'ronecros']): + return False + invalid_keywords = ['送檢單位', '切取日期', '固定日期', '檢查項目', '病理報告', '病歷號', '版本：', 'Result:', 'Comments:'] + if any(kw in t for kw in invalid_keywords): + return False + if any(t.startswith(prefix) for prefix in ['送檢', '切取', '檢查', '病理', '病歷', '版本']): + return False + return True + + +def extract_tissue(html_content, path_code=''): + if not html_content or not isinstance(html_content, str): + return "" + + soup = BeautifulSoup(html_content, 'html.parser') + + tissue = "" + # 1. Check lblTissue span + span = soup.find('span', id=lambda x: x and 'lblTissue' in x) + if span and is_valid_tissue(span.text): + tissue = span.text.strip() + + # 2. Check table row with 組織由來 + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + if '組織由來' in ths: + idx = ths.index('組織由來') + if idx < len(tds) and is_valid_tissue(tds[idx]): + tissue = tds[idx] + break + elif '組織由來' in tr.text: + if tds and is_valid_tissue(tds[-1]): + tissue = tds[-1] + + # 3. Check 器官與術式 / 器官 / 部位 / 檢體部位 + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + for header in ['器官與術式', '器官', '部位', '檢體部位']: + if header in ths: + idx = ths.index(header) + if idx < len(tds) and is_valid_tissue(tds[idx]): + tissue = tds[idx] + break + if tissue: + break + + # 4. Dig site from 組織學診斷 row if tissue is still empty + if not tissue: + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + if any('組織學診斷' in th for th in ths): + if tds: + val = tds[-1] + if val: + s = re.sub(r'^\d+[\.\s]*', '', val) + split_site = re.split(r',\s*(?:\d+(?:\-\d+)?\s*cm(?:\s+AAV|\s+above\s+anal\s+verge|\s+[A-Za-z]+)?|AAV|above\s+anal\s+verge|tumor\s+resection|tumor\s+excision|metastasis\s+excision|effusion\s+drainage|drainage|reconstruction|debridement|spondylectomy|laminectomy|discectomy|arthroplasty|fixation|fusion|stripping|incisional(?:\s+biopsy)?|excisional(?:\s+biopsy)?|punch(?:\s+biopsy)?|shave(?:\s+biopsy)?|wedge(?:\s+biopsy)?|core(?:\s+biopsy)?|site\s+not\s+state(?:d)?|site\s+unspecified|\?\s*side|\?|craniotomy(?:\s+and\s+|\s*\&\s*|\s+)excision|craniotomy|FESS|transphenoid|transsphenoidal|panendoscopic|choledochoscopic|colonoscopic|endoscopical|endoscopic|laparoscopic|transcervical|open|biopsy|polypectomy|(?:partial|subtotal|radical|wide|total)?\s*resection|excision|curettage|aspiration|dissection|cholecystectomy|hysterectomy|nephrectomy|lobectomy|mastectomy|meningioma|lipoma|fibroma|adenoma|carcinoma|polyp|hyperplasia|cyst|gastritis|sinusitis|cholecystectomy|fibrosis)\b', s, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*(?:corpus|cervix)\b', split_site, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site) and split_site != val: + tissue = split_site.strip() + break + + # 5. Check active report tab link: + if not tissue: + a_tag = soup.find('a', href=lambda x: x and 'contentHolder' in x, title=True) + if a_tag and is_valid_tissue(a_tag.get('title')): + tissue = a_tag['title'].strip() + + # 6. Check selected option: + if not tissue: + opt = soup.find('option', selected=True) + if opt and opt.text: + m = re.search(r'\(([^()]+)\)$', opt.text) + if m and is_valid_tissue(m.group(1)): + tissue = m.group(1).strip() + + # 7. Dig from 檢查報告 (lblResult or textarea or full text) if still blank/dot + if not tissue: + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.get_text(separator='\n') if ta else (res_span.get_text(separator='\n') if res_span else soup.get_text(separator='\n'))).strip() + if raw_text: + m = re.search(r'(?:Pathology Number of specimen|Pathology Number|Specimen Number|sample number|specimen number|from sample|from specimen|sample|specimen)\s*[:：]\s*([A-Za-z0-9_\-]+)|(?:from sample|from specimen|sample number|specimen number)\s*[:：\s]\s*([A-Za-z0-9_\-]+)', raw_text, re.IGNORECASE) + matched_code = (m.group(1) or m.group(2)).strip() if m else None + if matched_code: + tissue = matched_code + else: + lines = [l.strip() for l in raw_text.splitlines() if l.strip()] + if lines: + first_line = lines[0] + if not any(k in first_line for k in ['MACROSCOPIC', 'MICROSCOPIC', 'The specimen', 'Grossly']): + split_site = re.split(r',\s*(?:\d+(?:\-\d+)?\s*cm(?:\s+AAV|\s+above\s+anal\s+verge|\s+[A-Za-z]+)?|AAV|above\s+anal\s+verge)\b', first_line, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*(?:[A-Za-z\s]+)?(?:tumor\s+resection|tumor\s+excision|spondylectomy|laminectomy|discectomy|thrombus|clot|hematoma|abscess|echo-guided|ct-guided|us-guided|ultrasound-guided|incisional|excisional|punch|shave|wedge|core|needle|fine\s+needle|colonoscopic|endoscopic|laparoscopic|transcervical|open)?\s*(?:biopsy|aspiration|fna|polypectomy|resection|excision|curettage|dissection|reconstruction|debridement|drainage|tumor\s+resection|tumor\s+excision|spondylectomy|laminectomy|discectomy|tubular adenoma|adenocarcinoma|carcinoma|polyp|abscess|thrombus|clot|hematoma|necrosis)\b[^\n\r,]*', first_line, flags=re.IGNORECASE)[0] + split_site = re.split(r',\s*labeled\s+as\b', split_site, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site): + tissue = split_site.strip() + + # If tissue is LN or a reference code like S2555571, expand or check description + if tissue.upper() == 'LN': + tissue = 'Lymph node' + + if not tissue: + span = soup.find('span', id=lambda x: x and 'lblTissue' in x) + if span and span.text.strip().upper() == 'LN': + tissue = 'Lymph node' + + if tissue and re.match(r'^[A-Za-z0-9_\-]+$', tissue): + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.get_text(separator='\n') if ta else (res_span.get_text(separator='\n') if res_span else soup.get_text(separator='\n'))).strip() + if raw_text: + m = re.search(rf'{re.escape(tissue)}\s+([A-Za-z][^\n\r]+)', raw_text) + if m: + desc = m.group(1).strip() + split_site = re.split(r',\s*(?:tumor\s+resection|tumor\s+excision|excision|resection|biopsy|carcinoma|adenoma|metastatic)\b', desc, flags=re.IGNORECASE)[0] + if is_valid_tissue(split_site) and split_site != desc and not re.match(r'^[A-Za-z0-9_\-]+$', split_site): + tissue = split_site.strip() + + return tissue + + +def extract_short_diagnosis(html_content, path_code=''): + if not html_content or not isinstance(html_content, str): + return "" + + soup = BeautifulSoup(html_content, 'html.parser') + + # 1. Check table rows for 組織學診斷 / 診斷描述 + table_diags = [] + for tr in soup.find_all('tr'): + ths = [th.text.strip() for th in tr.find_all('th', recursive=False)] + tds = [td.text.strip() for td in tr.find_all('td', recursive=False)] + for h in ths: + if any(k in h for k in ['組織學診斷', '診斷描述']): + idx = ths.index(h) + if idx < len(tds): + val = tds[idx].text.strip() if hasattr(tds[idx], 'text') else str(tds[idx]).strip() + if val and val not in table_diags: + table_diags.append(val) + + if not table_diags: + for tr in soup.find_all('tr'): + cells = [td.text.strip() for td in tr.find_all(['th', 'td'])] + text = ' '.join(cells) + if any(k in text for k in ['組織學診斷', '診斷描述']): + tds = tr.find_all('td') + if tds: + val = tds[-1].text.strip() + if val and val not in table_diags: + table_diags.append(val) + + if table_diags: + return ', '.join(table_diags) + + # 2. Check textarea or lblResult + ta = soup.find('textarea') + res_span = soup.find('span', id=lambda x: x and 'lblResult' in x) + raw_text = (ta.text if ta else (res_span.text if res_span else '')).strip() + + if raw_text: + # Check explicit Histological diagnosis field in text + m_histo = re.search(r'Histological diagnosis[:：\s]\s*([^\*\n\r]+)', raw_text, re.IGNORECASE) + if m_histo and m_histo.group(1).strip(): + return m_histo.group(1).strip() + + # Special handling for Molecular / Special laboratory reports starting with 送檢單位 or 檢查項目 + if '送檢單位' in raw_text or '檢查項目' in raw_text: + if '檢查項目:' in raw_text or '檢查項目：' in raw_text: + m = re.search(r'檢查項目[:：]\s*(.*?)(?:Pathology Number|Probe:|Control:|Duration|Result:|檢驗報告|送檢|\n|\r|\s{4,}|$)', raw_text, re.DOTALL) + if m and m.group(1).strip(): + item = m.group(1).strip() + m_res = re.search(r'(?:Result:|檢驗報告[:：])\s*(.*?)(?:Procedures:|Comments:|參考文獻|Ref:|\n\n|\r\n\r\n|\s{4,}|$)', raw_text, re.DOTALL) + if m_res and m_res.group(1).strip(): + res_val = m_res.group(1).strip().replace('\n', ' ').replace('\t', ' ') + res_val = re.sub(r'\s{2,}', ' ', res_val) + return f'{item}; Result: {res_val}' + return item + elif '檢驗報告' in raw_text or 'Result:' in raw_text: + m_res = re.search(r'(?:Result:|檢驗報告[:：])\s*(.*?)(?:Procedures:|Comments:|參考文獻|Ref:|\n\n|\r\n\r\n|\s{4,}|$)', raw_text, re.DOTALL) + if m_res and m_res.group(1).strip(): + res_val = m_res.group(1).strip().replace('\n', ' ').replace('\t', ' ') + return re.sub(r'\s{2,}', ' ', res_val) + + # Standard Pathology Report - return full first paragraph before macroscopic/gross section + cut_patterns = [ + r'The specimen submitted', r'Grossly', r'Microscopically', r'Pathology Number', + r'送檢單位', r'檢查項目', r'Result:', r'Comments:', r'Ref:', r'\[\d+\]\.' + ] + text_cut = re.split('|'.join(cut_patterns), raw_text, flags=re.IGNORECASE)[0] + parts = re.split(r'\n|\r|\t|\s{2,}', text_cut) + non_empty = [p.strip() for p in parts if p.strip()] + if non_empty: + return non_empty[0] + + # 3. Fallback to lblBedDiagnosis if available + bed_span = soup.find('span', id=lambda x: x and 'lblBedDiagnosis' in x) + if bed_span and bed_span.text.strip(): + return bed_span.text.strip() + + return "" + + +def classify_site(tissue, diagnosis, html=''): + site_text = (str(tissue) + ' ' + str(diagnosis)).lower() + site_text = re.sub(r'\bpd\-?l1\b', '', site_text, flags=re.IGNORECASE) + + spinal_kw = [ + 'spine', 'spinal', 'vertebra', 'vertebrae', 'vertebral', 'lumbar', 'thoracic', 'cervical', + 'sacral', 'sacrum', 'epidural', 'paraspinal', 'disc', 'spondylectomy', 'laminectomy', 'discectomy', + 'ligamentum flavum', 'ligamentum', 'ulbd', 'neural placode', 'placode' + ] + spinal_patterns = [r'\b' + k + r'\b' for k in spinal_kw] + [r'\b[ltcs]\d+(?:[\-\/]\d+)?\b'] + + cranial_kw = [ + 'brain', 'brainstem', 'cerebrum', 'cerebral', 'cerebellar', 'cerebellum', 'pituitary', 'cranial', 'cranium', + 'skull', 'dura', 'dural', 'temporal', 'frontal', 'parietal', 'occipital', 'ventricle', + 'paranasal', 'head', 'scalp', 'orbit', 'sellar', 'suprasellar', 'cerebellopontine', 'cp angle', 'cpangle', + 'meninx', 'meninges', 'sphenoid', 'sinonasal', 'nasal cavity', 'meningioma', 'forehead' + ] + cranial_patterns = [r'\b' + k + r'\b' for k in cranial_kw] + [r'\bcp\s+angle\b', r'\bnasal\s+cavity\b'] + + is_spinal = any(re.search(p, site_text) for p in spinal_patterns) + is_cranial = any(re.search(p, site_text) for p in cranial_patterns) + + if 'dura mater' in site_text or 'dural' in site_text and 'epidural' not in site_text: + is_cranial = True + + if is_spinal and not is_cranial: + return 'spinal' + elif is_cranial and not is_spinal: + return 'cranial' + elif is_spinal and is_cranial: + if any(k in site_text for k in ['brain', 'cerebrum', 'dura mater']): + return 'cranial' + return 'spinal' + + return 'other' + + +def classify_type(tissue, diagnosis, html=''): + diag_str = str(diagnosis).lower() + text = (str(tissue) + ' ' + diag_str).lower() + + if any(k in diag_str for k in ['no evidence of', 'negative for', 'free of', 'no tumor', 'without tumor', 'no residual']): + return 'other' + + if any(k in diag_str for k in ['necrotic tissue', 'necrosis', 'organizing hematoma', 'hematoma', 'thrombus', 'clot', 'abscess', 'infarction', 'fibrosis', 'sinusitis', 'cholecystitis', 'gastritis', 'varicose']): + definitive_tumor = ['carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', 'osteoma', 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'subependymoma', 'ependymoma', 'lipoma', 'fibroma', 'papilloma', 'lymphoma', 'melanoma', 'schwannoma', 'malignancy', 'malignant'] + if not any(k in diag_str for k in definitive_tumor): + return 'other' + + non_tumor_findings = ['granuloma', 'inflammatory cell infiltration', 'organizing hematoma', 'hematoma', 'thrombus', 'clot', 'abscess', 'necrosis', 'infarction', 'fibrosis', 'sinusitis', 'cholecystitis', 'gastritis', 'varicose', 'amyloid angiopathy'] + neoplastic_findings = [ + 'carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', 'osteoma', 'chondroma', 'hemangioma', 'neuroma', 'leiomyoma', 'myxoma', + 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'subependymoma', 'ependymoma', 'ganglioglioma', 'oligodendroglioma', 'medulloblastoma', 'chordoma', 'hemangioblastoma', 'craniopharyngioma', 'lipoma', 'fibroma', 'papilloma', + 'polyp', 'hyperplasia', 'metastasis', 'metastatic', 'malignancy', 'malignant', + 'schwannoma', 'neurofibroma', 'melanoma', 'lymphoma', 'plasmacytosis', 'neoplastic', 'who grade', 'cns who grade' + ] + + if any(k in diag_str for k in non_tumor_findings) and not any(k in diag_str for k in neoplastic_findings): + return 'other' + + tumor_kw = [ + 'malignancy', 'malignant', 'osteoma', 'chondroma', 'hemangioma', 'neuroma', 'leiomyoma', 'myxoma', + 'subependymoma', 'ependymoma', 'ganglioglioma', 'oligodendroglioma', 'medulloblastoma', 'chordoma', 'hemangioblastoma', 'craniopharyngioma', 'who grade', 'cns who grade', 'tumor', 'tumour', 'carcinoma', 'adenocarcinoma', 'adenoma', 'sarcoma', 'osteosarcoma', + 'astrocytoma', 'glioma', 'meningioma', 'glioblastoma', 'lipoma', 'fibroma', 'papilloma', 'cyst', + 'polyp', 'hyperplasia', 'mass', 'lesion', 'neoplasm', 'metastasis', 'metastatic', + 'schwannoma', 'neurofibroma', 'melanoma', 'lymphoma', 'plasmacytosis', 'neoplastic' + ] + + if any(k in text for k in tumor_kw): + return 'tumor' + + return 'other' + + +def process_extraction(input_excel=None, output_csv=None, limit=None, mode='all'): + if input_excel is None: + input_excel = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + if not os.path.exists(input_excel) and os.path.exists('registry_pathologyreport.xlsx'): + input_excel = 'registry_pathologyreport.xlsx' + + if output_csv is None: + output_csv = os.path.join(script_dir, 'registry_pathologyreport_extracted.csv') + + print(f"Opening Excel file: {input_excel} ...") + wb = openpyxl.load_workbook(input_excel, read_only=True) + ws = wb.active + + headers = None + chart_idx = None + path_idx = None + html_idx = None + + all_raw_rows = [] + + print(f"Reading Excel rows...") + for i, row in enumerate(ws.iter_rows()): + vals = [cell.value for cell in row] + if headers is None: + headers = vals + chart_idx = headers.index('ChartNo') + path_idx = headers.index('PathCode') + html_idx = headers.index('html') + continue + + all_raw_rows.append(vals) + + if mode == 'last' and limit: + raw_rows = all_raw_rows[-limit:] + print(f"Processing last {len(raw_rows)} rows (out of {len(all_raw_rows)} total)...") + else: + raw_rows = all_raw_rows[:limit] if limit else all_raw_rows + print(f"Processing {len(raw_rows)} rows...") + + # Build full PathCode map from all Excel rows for fallback resolution + excel_pathcode_html_map = {} + for vals in all_raw_rows: + pcode = str(vals[path_idx]).strip() if vals[path_idx] else "" + hval = vals[html_idx] or "" + if pcode: + excel_pathcode_html_map[pcode] = hval + + rows = [] + pathcode_diag_map = {} + + for vals in raw_rows: + chart_no = vals[chart_idx] + path_code = str(vals[path_idx]).strip() if vals[path_idx] else "" + html_content = vals[html_idx] or "" + + tissue = extract_tissue(html_content, path_code) + diagnosis = extract_short_diagnosis(html_content, path_code) + + site = classify_site(tissue, diagnosis, html_content) + ttype = classify_type(tissue, diagnosis, html_content) + + row_dict = { + 'chart_no': chart_no, + 'path_code': path_code, + 'site': site, + 'type': ttype, + 'tissue': tissue, + 'diagnosis': diagnosis + } + rows.append(row_dict) + + if path_code and diagnosis: + pathcode_diag_map[path_code] = diagnosis + + print("Resolving empty diagnosis rows via 組織由來 (tissue) -> 病理號 (PathCode) matching...") + fallback_count = 0 + for r in rows: + if not r['diagnosis'] and r['tissue']: + tissue = r['tissue'].strip() + potential_codes = [tissue] + re.findall(r'[A-Za-z0-9_\-]+', tissue) + for code in potential_codes: + if code not in pathcode_diag_map and code in excel_pathcode_html_map: + ref_html = excel_pathcode_html_map[code] + if ref_html: + db_diag = extract_short_diagnosis(ref_html, code) + if db_diag: + pathcode_diag_map[code] = db_diag + + if code in pathcode_diag_map and pathcode_diag_map[code]: + r['diagnosis'] = pathcode_diag_map[code] + r['site'] = classify_site(r['tissue'], r['diagnosis']) + r['type'] = classify_type(r['tissue'], r['diagnosis']) + fallback_count += 1 + break + + print(f"Fallback resolution complete: updated {fallback_count} empty rows.") + print(f"Writing to CSV file: {output_csv} ...") + + with open(output_csv, 'w', newline='', encoding='utf-8-sig') as f: + writer = csv.writer(f) + writer.writerow(['病歷號', '病理號', 'site', 'type', '組織由來', 'pathological diagnosis']) + + for r in rows: + writer.writerow([r['chart_no'], r['path_code'], r['site'], r['type'], r['tissue'], r['diagnosis']]) + + print(f"Successfully exported {len(rows)} rows to {output_csv}") + + +if __name__ == '__main__': + inp = sys.argv[1] if len(sys.argv) > 1 else None + out = sys.argv[2] if len(sys.argv) > 2 else None + lim = int(sys.argv[3]) if len(sys.argv) > 3 and sys.argv[3].isdigit() else None + m = sys.argv[4] if len(sys.argv) > 4 else 'all' + process_extraction(inp, out, limit=lim, mode=m) diff --git a/patho/fetch.py b/patho/fetch.py new file mode 100755 index 0000000..f21729c --- /dev/null +++ b/patho/fetch.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import os +import sys +import re + +# Add repository root and 'ntuh' directory to sys.path +script_dir = os.path.dirname(os.path.abspath(__file__)) +base_dir = os.path.dirname(script_dir) +ntuh_dir = os.path.join(base_dir, 'ntuh') + +if base_dir not in sys.path: + sys.path.insert(0, base_dir) +if ntuh_dir not in sys.path: + sys.path.insert(0, ntuh_dir) + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ntuh.settings') + +import django +django.setup() + +import pandas as pd +from registry.models import PathologyReport + +illegal_re = re.compile(r'[\x00-\x08\x0b-\x0c\x0e-\x1f]') + + +def clean_string_for_excel(val): + if not isinstance(val, str): + return val + # Remove illegal XML / openpyxl control characters + val = illegal_re.sub('', val) + # Strip unnecessary head, style, script, meta, link tags so report body fits within cell limits + h = re.sub(r'<(?:head|style|script|meta|link)[^>]*?>.*?', '', val, flags=re.DOTALL | re.IGNORECASE) + h = re.sub(r'<(?:meta|link)[^>]*?>', '', h, flags=re.IGNORECASE) + h = re.sub(r'\n\s*\n', '\n', h) + return h.strip() + + +def fetch_pathology_reports(output_file=None): + if output_file is None: + output_file = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + + print(f"Fetching PathologyReport records from database...") + qs = PathologyReport.objects.all().values() + data = list(qs) + + print(f"Cleaning string fields & HTML body for Excel cell limit...") + for item in data: + for k, v in item.items(): + if isinstance(v, str): + item[k] = clean_string_for_excel(v) + + df = pd.DataFrame(data) + print(f"Fetched {len(df)} rows. Writing to {output_file}...") + df.to_excel(output_file, index=False) + print(f"Successfully exported {len(df)} rows to {output_file}") + + +if __name__ == '__main__': + default_output = os.path.join(script_dir, 'registry_pathologyreport.xlsx') + target_path = sys.argv[1] if len(sys.argv) > 1 else default_output + fetch_pathology_reports(target_path)