AIMC Topic: Neoplasm Staging

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Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical var...

Robotic omentectomy in gynecologic oncology: surgical anatomy, indications, and a technical approach.

Journal of robotic surgery
An omentectomy is a standard component care of gynecological cancers, particularly for surgical staging and treatment for malignant ovarian neoplasms, borderline tumors, fallopian tube cancers, primary peritoneal cancers as well as certain histologic...

Prognostic features of upstaged pT3a renal tumors with fat invasion after robot-assisted partial nephrectomy: is it time for a new subclassification?

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: The clinical management of pT3a pathologic-upstaged renal cell carcinoma (RCC) patients is actually controversial. Aim of this study was i) to assess the impact of pT3a upstaging on oncologic outcomes after robot-assisted partial nephre...

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Computational and mathematical methods in medicine
BACKGROUND: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a nov...

A nomogram to predict pathologic T2 stage in candidates to robot-assisted radical prostatectomy with iT3 prostate cancer on preoperative multiparametric MRI: results from a multi-institutional collaboration.

Minerva urology and nephrology
In candidates to robot-assisted radical prostatectomy (RARP) for locally advanced (iT3) prostate cancer on preoperative MRI, the performance of MRI for local staging is demonstrably suboptimal, and currently no prediction tools that might help surgeo...

Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.

BMC medical informatics and decision making
BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting primary lung cancer sites an...

Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer.

Sensors (Basel, Switzerland)
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging sys...

Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence.

Techniques in coloproctology
BACKGROUND: Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using ...

A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) using the Surveillance, Epidemiology, and End Results (SEER) database.