Clinical trials: Whole Slide Image
Non-small Cell Lung Cancer, Neoadjuvant Chemoimmunotherapy
Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
- Conditions
- Non-small Cell Lung Cancer, Neoadjuvant Chemoimmunotherapy, Complete Pathological Response
- Trial name
- NCT05925751
- Testing
- CT/PET/WSI-based Deep Learning Signature
- Where
- 3 sites in China
- Volunteers
- 100 people
- Sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
Prostatic Neoplasms, Lymphatic Metastasis
Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
The goal of this diagnostic test is to prospectively test the performance of pre-developed artificial intelligence (AI) diagnostic model for detecting pathological lymph node metastasis (LNM) of prostate cancer.
- Trial name
- NCT06253065
- Testing
- Artificial intelligence (AI)-based diagnostic model (developed)
- Where
- China
- Volunteers
- 225 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Bladder Cancer
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis.
- Trial name
- NCT06389019
- Testing
- Deep learning system for prognostication prediction in bladder cancer
- Where
- China
- Volunteers
- 1,000 people
- Sponsor
- Mingzhao Xiao
Prostatic Neoplasms, Castration-Resistant
Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer
The goal of this predictive test is to prospectively test the performance of pre-developed artificial intelligence (AI) predictive model for predicting the time to castration resistance of prostate cancer.
- Trial name
- NCT07376057
- Testing
- Artificial intelligence (AI)-based predictive model (developed)
- Where
- China
- Volunteers
- 150 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Prostate Cancer, Prostate Intraductal Carcinoma
Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer
The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the…
- Conditions
- Prostate Cancer, Prostate Intraductal Carcinoma, Prostate Cancer Aggressiveness, Prostate Cancer Stage
- Trial name
- NCT06662708
- Testing
- Accurate Prediction Artificial Intelligence Models
- Where
- China
- Volunteers
- 200 people, healthy volunteers welcome
- Sponsor
- Shao Pengfei
Thyroid Nodule
Multimodal Assessment of Malignancy in Atypia of Undetermined Significance in Thyroid Nodules Using Ultrasound and Cytology Whole-Slide Images
The prospective study aim to develop a multimodal deep learning model that integrates ultrasound images and cytological whole-slide images for more accurate malignant risk prediction of Bethesda III thyroid nodules.
- Trial name
- NCT07488325
- Testing
- Histopathology or cytopathology.
- Where
- China
- Volunteers
- 396 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
UTUC
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma
Upper Tract Urothelial Carcinoma (UTUC), characterized by its anatomical complexity and often aggressive clinical behavior, presents substantial difficulties in accurate diagnosis and reliable prognostication.
- Trial name
- NCT06993779
- Testing
- Deep learning system for prognostication prediction in upper tract urothelial carcinoma
- Where
- China
- Volunteers
- 1,000 people
- Sponsor
- Mingzhao Xiao
Lung Adenocarcinoma, Whole Slide Image
WSI Based DL for Diagnosing the IASLC Grading System of Lung Adenocarcinoma
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.
- Conditions
- Lung Adenocarcinoma, Whole Slide Image, IASLC Grading System, Artificial Intelligence
- Trial name
- NCT05925764
- Testing
- Whole Slide Image based Deep Learning
- Where
- 3 sites in China
- Volunteers
- 200 people
- Sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
Gastric Intestinal Metaplasia, Artificial Intelligence
Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System
The OLGIM staging system is highly recommended for a comprehensive assessment of GIM severity to evaluate patients' gastric cancer risk.
- Conditions
- Gastric Intestinal Metaplasia, Artificial Intelligence, Pathology, Gastric Cancer
- Trial name
- NCT05447221
- Testing
- The diagnosis of Artificial Intelligence and pathologists
- Where
- China
- Volunteers
- 150 people
- Sponsor
- Shandong University
Cancer, Lymphatic Metastasis
Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
The goal of this diagnostic test is to develop an artificial intelligence (AI)-based pan-cancer universal diagnostic model for detecting pathological lymph node metastasis (LNM), and prospectively evaluate its apllication value…
- Trial name
- NCT06517979
- Testing
- Artificial intelligence (AI)-based diagnostic model
- Where
- China
- Volunteers
- 10,000 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University