Neoplasms

Digestive System Diseases

All Conditions

Rare Diseases

Stomach Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Stomach Diseases

Stomach Cancer

This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen.

Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.

Neoadjuvant Chemotherapy

Di Dong, Ph.D.

Xinming Zhao

Zhenyu Jin

Lei Tang

Yi Wang

Weihua Liao

Yangming Li

Changming Huang

Shuzhong Cui

Shenping Yu

Guoxin Li

Xiaochun Meng

Zhenhui Li

Guanqiao Jin

Hexiang Wang

Jun Zhang

Zhenning Wang

Jie Bao

Zhaoxiang Ye

Jing Li

Jianbo Gao

Xiuhong Shan

Francesco De Cobelli

Yali Zang, Ph.D.

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants.

Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset.

Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.

Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.

Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.

TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.

Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.

The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and Eosin) staining for pathology imaging.

AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.

The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.

Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.

The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.

The duration from the date of patient confirmation to the date of death of the patient.

Peking University Cancer Hospital & Institute

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Yunnan Cancer Hospital

Henan Cancer Hospital

Zhenjiang First People's Hospital

First Hospital of China Medical University

Cancer Hospital of Guangxi Medical University

Peking University People's Hospital

Tianjin Medical University Cancer Institute and Hospital

The First Affiliated Hospital of Zhengzhou University

Nanfang Hospital, Southern Medical University

The Affiliated Hospital of Qingdao University

Ruijin Hospital

Sixth Affiliated Hospital, Sun Yat-sen University

Peking Union Medical College Hospital

Xiangya Hospital of Central South University

Affiliated Cancer Hospital & Institute of Guangzhou Medical University

The First Affiliated Hospital of Soochow University

First Affiliated Hospital, Sun Yat-Sen University

Fujian Medical University Union Hospital

Fujian Cancer Hospital

San Raffaele University Hospital, Italy

Chinese Academy of Sciences

Professor

Skin and Connective Tissue Diseases

Breast Neoplasms

Carcinoma

Carcinoma, Lobular

Carcinoma, Ductal, Breast

Carcinoma, Ductal

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Breast Diseases

Skin Diseases

Adenocarcinoma

Radiomic, pathomic, and clinical markers

William T. Tran, MRT(T), PhD

Kasia Jerzak, M.Sc., MD

Fang-I Lu, MD, FRCPC

This study examines retrospective clinical data on patients diagnosed with breast cancer and monitor their response to neoadjuvant chemotherapy, incidence of locoregional recurrence, distant metastasis, and disease-free survival. The hypothesis of this study is that breast cancer patients who achieve a pathological complete response (pCR) to neoadjuvant chemotherapy demonstrate distinct clinicopathomic biomarker signatures.

The specific aims of the study are to (1) to identify clinicopathomic biomarkers from pre-treatment core biopsies that are predictive of response to neoadjuvant chemotherapy, (2) determine patterns of metastasis from primary breast cancer to other distant sites, (3) determine the rates of local recurrence in breast cancer patients, and (4) to determine if there are significant radiomic, pathomic, and clinical markers for recurrence and distant metastasis.

Using Clinicopathomic Markers to Predict Neoadjuvant Chemotherapy Response in Breast Cancer

All data will be anonymized. Digital pathology images and anonymized clinical data will be deposited into the National Cancer Institute (NCI) data sharing repository, including The Cancer Imaging Archive (TCIA). Digital pathology images will also be annotated to facilitate analysis for other investigators. Computational methods, source-codes and instructions will be deposited to the open-source coding repository, GitHub (www.github.com).

Evaluating the degree of absence of residual cancer cells

Evaluating the time until a recurrence event has occurred in the breast

Sunnybrook Health Sciences Centre

Radiation Therapist Clinician Scientist

Symptoms and General Pathology

Recurrence

Carcinoma, Squamous Cell

Squamous Cell Carcinoma of Head and Neck

Head and Neck Neoplasms

Neoplasms, Squamous Cell

Disease Attributes

William Tran, MRT(T), PhD

Here, the investigators will develop a clinicopathomic assay from biomarkers obtained from digital pathologies of resected whole-mount oral cavity and oropharyngeal squamous cell carcinoma (OCSCC and OPSCC) specimens with the goal of administering personalized novel image-guided therapies immediately after primary surgical management in OCSCC and OPSCC patients. The primary aim is to determine the association between clinicopathomic biomarkers and LRR. The secondary aim is to develop a clinicopathomic risk score (assay) such that a decision-support tool can be used by physicians for measuring the benefit of additional therapies (i.e. conventional chemotherapy +/- radiation or administering dose-escalated chemoradiation) in the adjuvant setting to reduce LRR rates.

The proposed study is significant for developing personalized treatments. The proposed research will yield high rewards to patients by providing additional information to clinicians for better prognostication and potentially adapting adjuvant treatments to improve the survival of patients with OCSCC or OPSCC.

Analysis of Patterns of Recurrence in Head and Neck Cancer Using Clinicopathomic Markers

Evaluating the time until a recurrence event has occurred in the oral cavity or oropharyngeal region

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Endometrial Neoplasms

Uterine Neoplasms

Genital Neoplasms, Female

Urogenital Neoplasms

Uterine Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

The POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation.

The mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype

The expression of p53 was detected by immunohistochemistry. The abnormality of p53 protein expression (completely negative or diffusely strong positive in the nucleus) or expression location (cytoplasmic expression) was judged as p53abn, otherwise it was p53wt.

First, the mismatch repair (MMR) proteins were detected by immunohistochemistry, and the deletion of one or more proteins was classified as d-MMR subtype; Then the POLE gene mutation detection was performed, and the mutation Changes were classified as POLE mutation; Finally, p53 was detected by immunohistochemistry, and p53 mutant (p53 abn) and p53 wild-type (p53wt) were distinguished.

next generation sequencing AND Immunohistochemical examination

Jian Chen, Master

Molecular typing provides accurate information for the diagnosis, treatment and prognosis prediction of endometrial cancer, which has important clinical significance. However, due to its high cost and complicated process, it is difficult to be widely used in clinical practice. Based on the artificial intelligence method, this study fused the characteristics of MRI radiomics and pathomics, combined with the clinical pathological information, built a model to predict the molecular typing and prognosis, analyzed the biological characteristics of endometrial cancer from the multi-scale level, guided the personalized and precise diagnosis and treatment, in order to improve the prognosis of patients.

In this project, 150 cases of endometrial cancer were retrospectively collected, and 200 cases of endometrial cancer will be prospectively collected. All patients were pathologically confirmed and underwent Promise molecular typing. Before treatment, all patients completed abdominal MRI. Based on artificial intelligence technology, image features were extracted from magnetic resonance imaging, pathological features were extracted from pathological data, and clinical pathological data were collected at the same time. The treatment effect, recurrence and metastasis of patients were followed up, and the five-year survival rate and five-year progression free survival rate were calculated. It is proposed to focus on the following research:

1. Construction of molecular typing and prognosis prediction model of endometrial cancer based on magnetic resonance imaging Radiomics
2. Construction of molecular typing and prognosis prediction model of endometrial cancer based on pathomics.
3. Construction of a prediction model for molecular typing of endometrial cancer by integrating pathomics and radiomics.

MRI Radiomics Combined With Pathomics on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer

Study on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer Using a Model Constructed by Magnetic Resonance Imaging Radiomics Combined With Pathomics

All relevant patient personal information and follow-up results of this study were saved by the principal investigator, and there was no plan to share them with other investigators

The pathomics features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.

The imaging and pathological features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.

The imaging features of endometrial cancer patients were extracted by artificial intelligence method. Combined with clinicopathological risk factors and survival time, an imaging nomogram was constructed by lasso regression method to predict the molecular classification and prognosis of endometrial cancer. ROC curve was used to evaluate the test efficiency of the model.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy	NCT06035250	Gastric Cancer Image Pathology	Neoadjuvant Che...	18 Years -	Chinese Academy of Sciences
Using Clinicopathomic Markers to Predict Neoadjuvant Chemotherapy Response in Breast Cancer	NCT04021069	Breast Cancer Invasive Ductal... Invasive Lobula...	Radiomic, patho...	18 Years -	Sunnybrook Health Sciences Centre
AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy	NCT06035250	Gastric Cancer Image Pathology	Neoadjuvant Che...	18 Years -	Chinese Academy of Sciences
Analysis of Patterns of Recurrence in Head and Neck Cancer Using Clinicopathomic Markers	NCT04086849	Head and Neck N... Squamous Cell C...	Radiomic, patho...	18 Years -	Sunnybrook Health Sciences Centre
MRI Radiomics Combined With Pathomics on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer	NCT06126393	Endometrial Neo...	next generation...	18 Years - 80 Years	Fujian Cancer Hospital
Analysis of Patterns of Recurrence in Head and Neck Cancer Using Clinicopathomic Markers	NCT04086849	Head and Neck N... Squamous Cell C...	Radiomic, patho...	18 Years -	Sunnybrook Health Sciences Centre

Spots Global Cancer Trial Database for pathomics

Every month we try and update this database with for pathomics cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for pathomics

Every month we try and update this database with for pathomics cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for pathomics cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.