Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Li Sijin, MD

Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET). It is reported that the features related to treatment response, outcomes, tumor staging, tissue identification, and cancer genetics. Therefore, the investigators try to explore the key features for the outcome of lung cancer patients.

Radiomic Features:

PET/CT images, including other kinds of CT serials, were transported into a personal computer. Using the open source software of 3D-Slicer, volumes of interest (VOIs) for primary tumor, or even lymph nodes, was semi-automatically or manually segmented. And then, radiomic features were extracted.

PET Parameters:

Using combined CT VOIs, corresponding PET standard uptake value (SUV, no unit) were measured. For a foci (either tumor, or lymph node), mean, sum and maximum SUV were documented, and were used for training and validating models alongside radiomic features.

Feature Selection:

Data were analyzed by deep learning or random forests method, and top 20 variables were scored by their contribution to the regression (variable importance, VIMP). The generalized features were identified as the same ones between two kinds of image serials (for example, ordinary and thin-section CT, or PET and CT). Additionally, when three or more features met the criterion, a lower value of Akaike information criterion (AIC) which measures the relative quality of statistical models was used to find appropriate features with lower overfitting possibility.

Model Validation:

The developed model was validated internally and externally. The internal indices for independent continuous variable were accuracy (bias and absolute bias) and precision (correlation coefficient and R square), and that for independent classified or survival variable was c-index. The patients enrolled from another medical center were used for external validation.

Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients

The time from the scan date to death for any reason

The First Affiliated Hospital of Anhui Medical University

The First Affiliated Hospital of Shanxi Medical University

Chief physician

Skin and Connective Tissue Diseases

Breast Neoplasms

Breast Diseases

Skin Diseases

All Drugs and Chemicals

Estrogens

Progesterone

Luminal A：ER+ and/or PR+,HER2- Luminal B：ER+ and/or PR+,HER2+

\* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2

ER- PR-,HER2+

\* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2

ER- PR-,HER2-

\* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2

Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.

Multidisciplinary cooperative comprehensive treatment

Gu Ya Jia

Hua jia

Qian zhaoxia

Wang he

You chao

Zhuang zhiguo

Jiang ling

Zheng rencheng

Xiao qin

Chen yanqiong

Hu xiaoxin

1. Identify the imaging features of breast cancer with different molecular types
2. Reveal the association between hormone receptor positive/HER2 negative breast cancer and imaging histology, Oncotype Dx recurrence score
3. Combine genomics and imaging to establish a predictive model for the sensitivity of HER2-positive breast cancer targeted therapy
4. Establish an imaging genomics prediction model for triple-negative breast cancer molecular subtypes, and clarify the imaging genomics characteristics of the therapeutic targets of each subtype

Research design

1. Research on the molecular typing of breast cancer based on imaging features
2. Establish a Luminal breast cancer recurrence risk prediction model
3. Establish HER2 targeted therapy sensitivity prediction model
4. Establish TNBC molecular subtype prediction model Research methods Research Object This study used a multi-center study to prospectively enroll breast cancer patients diagnosed with pathology. All enrolled patients had complete clinical data, including demographic characteristics (gender, age, menstrual status and fertility history), and pathological data (histopathological data). Staging, immunohistochemical status and FISH, genetic testing records the recurrence score and genotype), imaging data, complete treatment and follow-up (whether there is local recurrence and metastasis, and the time of diagnosis).

Magnetic resonance examination In order to maintain the comparability between the images and reduce the systematic errors, each center selects a fixed MR device for scanning. Among them, a. Oncology Hospital chose to scan images with 3.0T (Siemens Skyra) MR equipment. A special breast coil is used to add high-definition diffusion-weighted scanning and multi-b value diffusion-weighted scanning before the dynamic enhancement scan. Dynamically enhanced acquisition in 5 phases with a time resolution of 65s. b. Renji Hospital uses Netherlands Philips Achieva 3.0 T superconductor MR scanner, 4-channel dedicated breast phased array coil. Scanning sequences include T1WI, T2WI, T2WI fat suppression, DWI and DCE-MRI. The contrast agent was Gd-DTPA, with a dose of 0.1 mmol/kg, an injection rate of 2.0 mL/s, and an additional 20 mL of saline was added to the tube after injection. The T1WI scan was performed first, and 5 time phases were continuously scanned after the injection of contrast agent, and each time phase was separated by 61 s, for a total of 6 time phases. c. Chinese women and babies are scanned with 1.5T SIEMENS AERA MR equipment and special breast coils. Scanning sequence includes 5 phases of T1WI, T2WI fat suppression, DWI and dynamic enhancement scan, time resolution 71s.

Image processing Use software to make semi-automatic and automatic outlines of the tumor interest area, and make the outline of the tumor solid enhancement part, the entire tumor area and the surrounding edema zone in the transverse position. In order to accurately delineate the tumor, compare the T1 and T2 weighted and dynamically enhanced images, two imaging physicians are responsible, one is responsible for delineation and the other is reviewed, and the disputed area is determined after discussion by a third person. Create a dynamic enhanced tumor texture analysis program to automatically extract imaging omics features in the region of interest. Using a labeled data set, a computer-based automatic segmentation algorithm model based on machine learning is constructed to automatically extract regions of interest, and segmentation performance evaluation is performed on manually delineated labels.

Statistical analysis Perform statistical analysis on the obtained images and clinical data, extract image omics features and use machine learning algorithms to screen important features. Use statistical tools such as SPSS and R language. Paired t test (continuous variable) and chi-square test (discontinuous variable) were used to compare the clinical and imaging characteristics of patients with different prognosis; correlation analysis was used to evaluate the imaging histology characteristics and different pathological tissue grades, Correlation between lymph node metastasis and specific gene expression; use Kaplan-Meier survival curve to analyze the prognostic difference between patients with different imaging omics characteristics, and use log-rank method to test the difference; use cox survival model to compare clinical characteristics and imaging omics The characteristics and prognosis of patients (tumor-free survival, progression-free survival, overall survival) were analyzed by multiple factors. Further, deep learning algorithms can be used to automatically learn imaging omics features that may be related to molecular subtypes and prognosis to build prediction models.

Clinical Study of Imaging Genomics Based on Machine Learning for BCIG

Clinical Study of Imaging Genomics Based on Machine Learning for Breast Cancer Molecular Typing and Risk Prediction (BCIG)

1. Build a model to predict molecular typing based on image
2. Establish a prediction model for predicting the risk of Luminal breast cancer recurrence
3. Establish a prediction model for predicting her2 targeted drug resistance
4. Establishing a triple-negative molecular model for breast cancer

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients	NCT03648151	Lung Cancer Image, Body		-	The First Affiliated Hospital of Shanxi Medical University
Clinical Study of Imaging Genomics Based on Machine Learning for BCIG	NCT04461990	Breast Cancer Molecular Typin... Pathology Image, Body	Multidisciplina...	-	Fudan University
Clinical Study of Imaging Genomics Based on Machine Learning for BCIG	NCT04461990	Breast Cancer Molecular Typin... Pathology Image, Body	Multidisciplina...	-	Fudan University

Spots Global Cancer Trial Database for image, body

Every month we try and update this database with for image, body 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 image, body

Every month we try and update this database with for image, body 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 image, body 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.