ArticleNature medicine2024
Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06525025 (Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database), which is not on this map. Cited by 40 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database: A Prospective Multicenter Cross-Sectional Study
Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Modeling metastasis and predicting drug response with malignant effusion-derived organoids: a systematic review and quantitative assessment.Journal of translational medicine · 2026Pooled it
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- Imaging of cancer of unknown primary: a systematic literature review of the past, present, and future.The British journal of radiology · 2025Pooled it
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- Deep learning of pretreatment ascites cytopathology for platinum-resistance risk stratification in advanced epithelial ovarian cancer.Neoplasia (New York, N.Y.) · 2026Article
- Few-Shot Learning for Cytological Classification of Primary Lung Cancer and Pulmonary Metastases During Endobronchial Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Application of a machine learning model integrating T cell subsets and clinical markers in 28-Day mortality prediction of sepsis patients.Clinical and experimental medicine · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images.NPJ digital medicine · 2026Article
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The role of artificial intelligence in advancing population-based cancer registration.Science bulletin · 2026Review
- Article
- Review
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Bridging radiology and pathology: domain-generalized cross-modal learning for clinical.NPJ digital medicine · 2026Article
- Beyond origin: multimodal AI synthesis to resolve cancers of unknown primary.Cancer biology & medicine · 2026Article
- Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors at 8 institutions in 2 countries.
Funding
Abstract
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
Indexed as
Identifiers
38627559PMC11108774W4394855233What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.