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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study

Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study

Surgery is the primary treatment for colon and rectal cancer [22], but due to its high postsurgical mortality rate, ongoing prognosis management is essential. Therefore, research aimed at extending patient survival is being conducted from various perspectives, including the prediction of overall survival, disease-free survival, and recurrence.

Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

JMIR Med Inform 2025;13:e75022

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Our project examined whether a small dataset for common colon polyp entities could be used to develop a robust and accurate ML model for diagnostic purposes using an Auto ML model from Google’s Vertex AI. Colon polyps are a precursor to invasive carcinoma, and a high-volume sample is encountered in the pathology lab.

David Beyer, Evan Delancey, Logan McLeod

JMIR Form Res 2025;9:e67457

Effectiveness and Acceptability of Targeted Text Message Reminders in Colorectal Cancer Screening: Randomized Controlled Trial (M-TICS Study)

Effectiveness and Acceptability of Targeted Text Message Reminders in Colorectal Cancer Screening: Randomized Controlled Trial (M-TICS Study)

Resultados de una cartera de servicios de salud pública en la farmacia comunitaria: cribado de cáncer de colon [Findings from a community pharmacy services portfolio of public health: colon cancer screening](httpscolon

Nuria Vives, Noemie Travier, Albert Farre, Gemma Binefa, Carmen Vidal, Maria Jose Pérez Lacasta, Gemma Ibáñez-Sanz, Ena Pery Niño de Guzmán, Jon Aritz Panera, Montse Garcia, M-TICS Research Group

JMIR Public Health Surveill 2024;10:e57959

Effect of the Nutraceutical Micodigest 2.0 on the Complication Rate of Colorectal Cancer Surgery With Curative Intent: Protocol for a Placebo-Controlled Double-blind Randomized Clinical Trial

Effect of the Nutraceutical Micodigest 2.0 on the Complication Rate of Colorectal Cancer Surgery With Curative Intent: Protocol for a Placebo-Controlled Double-blind Randomized Clinical Trial

Recent studies using animal models have shown substantial alterations in the composition of intestinal microbiota after colon resection [21]. Kong et al [22] evaluated the changes in gut microbiota using fecal samples from 43 CRC patients collected before and after surgery. After CRC surgery, the Bacteroidetes/Firmicutes ratio and the number of obligate anaerobes (including Bacteroides, Bifidobacterium, Faecalibacterium, Parabacteroides, and Prevotella) decreased [23].

Cristina Regueiro, Laura Codesido, Laura García-Nimo, Sara Zarraquiños, David Remedios, Arturo Rodríguez-Blanco, Esteban Sinde, Catalina Fernández-de-Ana, Joaquín Cubiella

JMIR Res Protoc 2022;11(5):e34292

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy

For additional analyses, such as subgroup analysis or meta-regression, the authors extracted the following variables from each included study: publication year, geographic origin of the data (ie, Western vs Asian), type of endoscopic images, type of CAD models, location of the DCPs (ie, any colon vs rectosigmoid colon), number of total images included, and type of test data sets (internal test vs external test).

Chang Seok Bang, Jae Jun Lee, Gwang Ho Baik

J Med Internet Res 2021;23(8):e29682

e-Learning for Instruction and to Improve Reproducibility of Scoring Tumor-Stroma Ratio in Colon Carcinoma: Performance and Reproducibility Assessment in the UNITED Study

e-Learning for Instruction and to Improve Reproducibility of Scoring Tumor-Stroma Ratio in Colon Carcinoma: Performance and Reproducibility Assessment in the UNITED Study

Staging of colon cancer by pathologists is based on hematoxylin and eosin (H&E)-stained sections of the primary tumor. The tumor-node-metastasis (TNM) classification is used as the main selection criterion for additional treatment, along with noting of characteristics such as depth of invasion and differentiation grade [1], according to the American Joint Committee staging algorithm. However, conventional H&E sections provide more information than previously recognized.

Marloes A Smit, Gabi W van Pelt, Elisabeth MC Dequeker, Raed Al Dieri, Rob AEM Tollenaar, J Han JM van Krieken, Wilma E Mesker, UNITED Group

JMIR Form Res 2021;5(3):e19408