Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Lung cancer patients with PET/CT or CT examination before any cancer-specific treatment

Yang Jin, MD

The investigators will evaluate the utility of computer aided image analysis in lung cancer with the aim of predicting treatment response and prognosis.

Tumor biological behavior is the fundamental cause of heterogeneous prognosis. The features found on medical images are also reflections of the tumor biological behavior. However, the limitations in spatial and intensity resolution of the naked eye are two inevitable shortcomings of image interpretation by naked eyes, resulting in subjective and limited analyses of images. Computer aided image analyses such as radiomic analysis and machine learning methods are emerging as promising image interpretation methods. The natural advantage of the unlimited spatial and intensity resolution of computers can overcome the shortcomings of visual inspection with the naked eye. Moreover, the massive computing power of computer is also far greater than that of humans. This study will focus on the application of computer aided analysis in predicting treatment response and prognosis in lung cancer.

Clinical Significance of Computer Aided Image Analysis in Treatment Response Evaluation of Lung Cancer

The interval between the date of diagnosis and death

The interval between the date of treatment initiation and disease progression

Wuhan Union Hospital, China

Professor

Skin and Connective Tissue Diseases

Symptoms and General Pathology

Breast Neoplasms

Triple Negative Breast Neoplasms

Pathologic Complete Response

Breast Diseases

Skin Diseases

Patients with triple-negative or HER2 Positive breast cancer, axillary lymph node metastasis who underwent NAC are eligible for this study. Axillary lymph node metastasis is confirmed by fine needle aspiration (FNA) or core needle biopsy (CNB) at initial diagnosis. CT-based radiomics will be uesd in evaluating the response and predicting pCR of metastatic lymph nodes after NAC in breast cancer patients.

Lize Wang, MD

Yingjian He, MD

Jiwei Wang, MD

This is a prospective, single-center, non-randomized, non-controlled observational study.

Almost 55-62% of patients with triple-negative or human epidermal growth factor receptor 2 (HER2) positive, node-positive breast cancer achieve an axillary pathologic complete remission (pCR) after neoadjuvant chemotherapy (NAC). To avoid surgery post-NAC, it is paramount to accurately identify patients who achieve pCR in axillary lymph node (ALN). We found that patients with normal-appearing lymph nodes on computed tomography (CT) based radiomics of the axilla after chemotherapy had a lower risk of developing residual nodal disease. However, the features of CT-based radiomics for pCR ALN following NAC has not been established yet. This study aimed to assess the performance of CT-based radiomics in evaluating the response and predicting pCR of metastatic lymph nodes after NAC in breast cancer patients.

Axillary lymph node status are determined in FNA/CNB and surgical specimen.

CT-based Radiomics of ALN pCR (ypN0) in Breast Cancer Undergoing NAC

Computed Tomography-based Radiomics of Axillary Lymph Node Pathological Complete Remission (ypN0) in Breast Cancer Undergoing Neoadjuvant Chemotherapy

Sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) of CT-based radiomics for axillary lymph node status after neoadjuvant chemotherapy will be assessed.

The diagnostic performance of CT-based radiomics for the evaluation of ALN after NAC was evaluated with receiver operating characteristic (ROC) curve analysis. The diagnostic accuracy was estimated by calculating the area under the ROC curve

Peking University

Director of Breast Center of Peking University Cancer Hospital

Antineoplastic Agents

All Drugs and Chemicals

Immune Checkpoint Inhibitors

Immunomodulating Agents

Patients who has received mono-therapy of immune checkpoint inhibitor.

Patients who has received immune checkpoint inhibitor combining chemotherapy.

Clinical: A diagnostic model incorporating clinical variables (Age, Pathological diagnosis, level of serum tumor biomarkers etc.)

Clinical

Semantic: A diagnostic model incorporating semantic radiological features (shape, location, border, density etc.)

Semantic

Radiomic: A diagnostic model incorporating quantitative radiomic features (histogram, texture, morphology etc.)

Radiomic

Zhaoxiang Ye, M.D, Ph.D

This study aims to investigate the feasibility and efficiency of CT radiomic analysis which serves as a high through-put analytical strategy applied to image big-data resource in evaluating and predicting the response of immunotherapeutics. A multi-center retrospective diagnostic test has been designed for this aim to compare the predictive performance of clinical model, qualitative model incorporating semantic CT features and image-based quantitative radiomic model. The reference standard of therapeutic effect is determined by the latest evaluation result utilizing iRECIST within 365 days after recruited. This study intends to enroll 400 participates who had been diagnosed with advanced somatic solid tumor confirmed by histo- or cyto-pathological examination and were planning to receive immunotherapy.

Radiomics of Immunotherapeutics Response Evaluation and Prediction

Radiomics of Immunotherapeutics Response Evaluation and Prediction in Solid Tumor: A Multicenter Diagnostic Study

Area under curve (AUC) of each diagnostic model

Incidence of immune-related adverse events

Tianjin Chest Hospital

Shandong Tumor Hospital

West China Hospital

First Affiliated Hospital of Zhejiang University

Sun Yat-sen University

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Liaoning Tumor Hospital & Institute

Hubei Cancer Hospital

First Affiliated Hospital Xi'an Jiaotong University

Tianjin Medical University Cancer Institute and Hospital

Carcinoma

Carcinoma, Squamous Cell

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Neoplasms, Squamous Cell

Randomly (121 cases) divided as the training and test sets in a 7:3 ratio.

Segmented into two groups based on the batched collected, which were defined as external validation set1 (n = 68) and external validation set2 (n = 130)

The predictive capability of the above Resnet50 deep learning (DL) model was validated in the test set. Based on the AUC and ACC, the best prediction model was identified. To explore the robust of the selected model, ROC analysis was performed the in the external validation set. Moreover, the Log-rank test was applied to evaluate the prognostic value of the model.

The Resnet50 deep learning (DL) model

Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions.

Aim: To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features.

Methods: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis.

10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma

Clinicopathological and Prognostic Analysis of Oral and Maxillofacial Squamous Cell Carcinoma: a Single-center 10-year Retrospective Study

The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC)

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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

Pharmaceutical Solutions

The mutational and copy number analyses will be complemented by transcriptomic profiling. RNA will be extracted from FFPE samples using miRNAeasy FFPE kit (Qiagen) and checked for quality and quantify by 2100 Bioanalyzer instrument (Agilent) and Qubit Fluorometer (ThermoFisher), respectively. Transcriptome analyses will be performed by RNA-seq. We will apply total RNAseq using the Illumina® TruSeq Stranded Total RNA workflow that provides a solution allowing the detection of whole transcriptome, splicing variants, and transcript fusions of human RNA isolated from FFPE samples. Libraries will be run using the Illumina's Novaseq6000 system, with a least 50 millions of reads/sample, the minimum read depth for the correct evaluation of low expressed transcripts.

trascriptomic profiling

francesco fanfani

The aim is to develop radiogenomics models to stratify patients into three main risk categories (Favorable, Intermediate, and Unfavorable) according to the ProMisE model (9) and use these models to predict the most prognostically relevant EC histopathological features (i.e. FIGO stage, degree of tumor differentiation, histotype, LVSI status, myometrial and cervical invasion, lymph node metastases).

These models would support clinicians in personalizing surgical and adjuvant treatment choice among the options considered by the international guidelines.

As far as the sample size concerns, the number of images required for the analysis are a figure of at least 100 positive cases (15). We considered as positive patients with a favorable prognostic profile according to PORTEC-4a and also low and intermediate risk class according to ESGO/ESTRO/ESP 2020 recommendations.Therefore, we estimate to enroll at least 1000 patients in order to reach the 100 positive cases.

Radiomics and Radiogenomics Models to Predict Molecular Integrated Risk Classes and Prognostic Factors in Endometrial Cancer.

Radiomics and Radiogenomics Models to Predict Molecular Integrated Risk Classes and Prognostic Factors in Endometrial Cancer ID: ROMANTIC STUDY

Receiver operating characteristic (ROC) curve and 95% confidence interval (CI) will be performed to determine cut-off values for the studied quantitative variables.

To test the validity of different clinical and ultrasound variables Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be determined

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Clinical Significance of Computer Aided Image Analysis in Treatment Response Evaluation of Lung Cancer	NCT03954847	Lung Cancer		18 Years -	Wuhan Union Hospital, China
CT-based Radiomics of ALN pCR (ypN0) in Breast Cancer Undergoing NAC	NCT04457700	Triple-negative... HER2-positive B...		18 Years - 70 Years	Peking University
Radiomics of Immunotherapeutics Response Evaluation and Prediction	NCT04079283	Solid Tumor Predictive Canc...	Clinical Semantic Radiomic	18 Years -	Tianjin Medical University Cancer Institute and Hospital
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma	NCT06366906	HNSCC AI Radiomic MRI	The Resnet50 de...	-	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma	NCT06366906	HNSCC AI Radiomic MRI	The Resnet50 de...	-	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Radiomics and Radiogenomics Models to Predict Molecular Integrated Risk Classes and Prognostic Factors in Endometrial Cancer.	NCT06279832	Endometrial Can...	trascriptomic p...	18 Years -	Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Clinical Significance of Computer Aided Image Analysis in Treatment Response Evaluation of Lung Cancer	NCT03954847	Lung Cancer		18 Years -	Wuhan Union Hospital, China

Spots Global Cancer Trial Database for radiomic

Every month we try and update this database with for radiomic 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 radiomic

Every month we try and update this database with for radiomic 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 radiomic 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.