ArticleAbdominal radiology (New York)2023
Differentiating peritoneal tuberculosis and peritoneal carcinomatosis based on a machine learning model with CT: a multicentre study.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Disseminated Tuberculosis Mimicking Metastatic Cancer During Ruxolitinib Therapy for Polycythemia Vera: A Case Report and Literature Review.The American journal of case reports · 2026Review
- Diagnostic Imaging of Extrapulmonary Tuberculosis Across Organ Systems.Diagnostics (Basel, Switzerland) · 2026Review
- MRI quantitative imaging biomarkers in differentiating brain parenchymal tuberculoma and lung cancer brain metastases.European journal of medical research · 2025Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Review
- CT and MR in peritoneal malignancies: pearls and pitfalls at preoperative examination.Insights into imaging · 2025Article
- Imaging in malignant peritoneal neoplasms.Abdominal radiology (New York) · 2025Review
- Case Report: Unveiling the hidden: a rare case of endometrial tuberculosis presenting as peritoneal carcinomatosis.Frontiers in oncology · 2025Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study.Abdominal radiology (New York) · 2024Article
- Clinical and Radiological Parameters to Discriminate Tuberculous Peritonitis and Peritoneal Carcinomatosis.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIt is still a challenge to make early differentiation of peritoneal tuberculosis (PTB) and peritoneal carcinomatosis (PC) clinically as well as on imaging and laboratory tests. We aimed to develop a model to differentiate PTB from PC based on clinical characteristics and primary CT signs.
methodsThis retrospective study included 88 PTB patients and 90 PC patients (training cohort: 68 PTB patients and 69 PC patients from Beijing Chest Hospital; testing cohort: 20 PTB patients and 21 PC patients from Beijing Shijitan Hospital). The images were analyzed for omental thickening, peritoneal thickening and enhancement, small bowel mesentery thickening, the volume and density of ascites, and enlarged lymph nodes (LN). Meaningful clinical characteristics and primary CT signs comprised the model. ROC curve was used to validate the capability of the model in the training and testing cohorts.
resultsThere were significant differences in the following aspects between the two groups: (1) age; (2) fever; (3) night sweat; (4) cake-like thickening of the omentum and omental rim (OR) sign; (5) irregular thickening of the peritoneum, peritoneal nodules, and scalloping sign; (6) large ascites; and (7) calcified and ring enhancement of LN. The AUC and F1 score of the model were 0.971 and 0.923 in the training cohort and 0.914 and 0.867 in the testing cohort.
conclusionThe model has the potential to distinguish PTB from PC and thus has the potential to be a diagnostic tool.
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