Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Gland and Hormone Related Diseases

All Conditions

Immune System Diseases

Rare Diseases

Ovarian Neoplasms

Carcinoma, Ovarian Epithelial

Hypersensitivity

Carcinoma

Endocrine Gland Neoplasms

Ovarian Diseases

Adnexal Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Genital Neoplasms, Female

Urogenital Neoplasms

Endocrine System Diseases

Gonadal Disorders

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Ovarian Cancer

Ovarian Epithelial Cancer

Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Radiomic Algorithm

Ruilin Lei, phD

Herui Yao, PhD

Yunfang Yu, MD

Platinum-sensitive is an important basis for the treatment of recurrent epithelial ovarian cancer (EOC) without effective methods to predict.We aimed to develop and validate the EOC deep learning system to predict the platinum-sensitive of EOC patients through analysis of enhanced magnetic resonance imaging (MRI) images before initial treatment.Ninety-three EOC patients received platinum-based chemotherapy (\>= 4 cycles) and debulking surgery from Sun Yat-sen Memorial Hospitalin China from January 2011 to January 2020 were enrolled. This deep-learning EOC signature achieved a high predictive power for platinum-sensitive, and the signature based on MRI whole volume is better than that on primary tumor area only.

Deep Learning Magnetic Resonance Imaging Radiomic Predict Platinum-sensitive in Patients With Epithelial Ovarian Cancer

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

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

Principal Investigator

Respiratory Tract (Lung and Bronchial) Diseases

Lung Neoplasms

Adenocarcinoma

Multiple Pulmonary Nodules

Adenocarcinoma in Situ

Carcinoma in Situ

All Drugs and Chemicals

Immunomodulating Agents

The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to December 31,2019. Patients with single pulmonary lesion underwent preoperative chest CT scan and histologically confirmed precancerous lesions or early stage lung adenocarcinoma after thoracic surgery was included.

The same inclusion/exclusion criteria were applied for another independent centers, Sun Yat-sen Memorial Hospital ,Guangdong Province, China, forming an external validation cohort of 73 patients

The same inclusion/exclusion criteria were applied for another independent centers, Zhoushan Lung Cancer Institution, Zhejiang Province, China, forming second external validation cohort of 30 patients

The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to May 31,2020. Patients with advanced lung cancer underwent preoperative chest CT scan and histologically confirmed NSCLC before receiving immunotherapy was included.

Haiyu Zhou, PhD

Luyu Huang

Yufang Yu

Hanbo Cao, PhD

Yunfang Yu

The purpose of this study was to investigate whether the combined radiomic model based on radiomic features extracted from focus and perifocal area (5mm) can effectively improve prediction performance of distinguishing precancerous lesions from early-stage lung adenocarcinoma, which could assist clinical decision making for surgery indication. Besides, response and long term clinical benefit of immunotherapy of advanced NSCLC lung cancer patients could also be predicted by this strategy.

Early detection and diagnosis of pulmonary nodules is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Deferential pathology results causes widely different prognosis after standard surgery among pulmonary precancerous lesion, atypical adenomatous hyperplasia (AAH) as well as adenocarcinoma in situ (AIS), and early stage invasive adenocarcinoma (IAC). The micro-invasion of pulmonary perifocal interstitium is difficult to identify from AIS unless pathology immunohistochemical study was implemented after operation，which may causes prolonged procedure time and inappropriate surgical decision-making. Key feature-derived variables screened from CT scans via statistics and machine learning algorithms, could form a radiomics signature for disease diagnosis, tumor staging, therapy response adn patient prognosis. The purpose of this study was to investigate whether the combined radiomic signature based on the focal and perifocal（5mm）radiomic features can effectively improve predictive performance of distinguishing precancerous lesions from early stage lung adenocarcinoma. Besides, immunotherapy response is various among patients and no more than 20% of patients could benefit from it. None reliable biomarker has been found yet expect Programmed death-ligand 1 (PD-L1) expression, the only approved biomarker for immunotherapy. However recent reports suggested that patients could benefit from immunotherapy regardless of PD-L1 positive or negative. On the contrast, radiomics has show it advantages of non-invasiveness, easy-acquired and no limitation of sampling. Therefore, we applied this strategy in prediction for the immunotherapy response of advanced NSCLC lung cancer patients receiving immune checkpoint inhibitors (ICIs), which would prevent some non-benefit patient from the adverse effect of ICIs.

CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC

Rate of ORR in all subjects for the patients who receiving immunotherapy

From enrollment to progression or death (for any reason) in immunotherapy cohort

From enrollment to death (for any reason) in immunotherapy cohort

Rate of CBR greater than or equal to 24 weeks in all subjects

Guangdong Provincial People's Hospital

Digestive System Diseases

Liver Neoplasms

Hemangioendothelioma

Hepatoblastoma

Hemangioma

Neoplasms, Vascular Tissue

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

The internal cohort was retrospectively enrolled in West China Hospital, Sichuan University from June 2010 and December 2020. It is a training and internal validation cohort.

The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.

Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Yuhan Yang, MD

Hepatic tumors in the perinatal period are associated with significant morbidity and mortality in affected patients. The conventional diagnostic tool, such as alpha-fetoprotein (AFP) shows limited value in diagnosis of infantile hepatic tumors. This retrospective-prospective study is aimed to evaluate the diagnostic efficiency of the deep learning system through analysis of magnetic resonance imaging (MRI) images before initial treatment.

Hepatic tumors seldom occur in the perinatal period. They comprise approximately 5% of the total neoplasms of various types occurring in the fetus and neonate. Infantile hemangioendothelioma is the leading primary hepatic tumor followed by hepatoblastoma. It should be mentioned that alpha-fetoprotein (AFP) is highly elevated during the first several months after birth even in normal infants, thus the diagnostic value of AFP is limited for infantile patients with hepatic tumors. This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available magnetic resonance imaging (MRI) images from June 2010 and December 2020. The investigators have constructed a deep learning radiomics diagnostic model on this retrospective cohort and validated it internally. A prospective cohort would recruit infantile patients diagnosed as liver tumor since January 2021. The proposed deep learning model would also be validated in this prospective cohort externally. The established model would be able to assist diagnosis for hepatic tumor in infants.

Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants

The diagnostic accuracy of infantile liver tumors with deep learning algorithm.

The diagnostic sensitivity of infantile liver tumors with deep learning algorithm.

The diagnostic specificity of infantile liver tumors with deep learning algorithm.

The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm.

The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Deep Learning Magnetic Resonance Imaging Radiomic Predict Platinum-sensitive in Patients With Epithelial Ovarian Cancer	NCT04511481	Predictive Canc...	Radiomic Algori...	22 Years - 99 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC	NCT04452058	Predictive Canc... Lung Cancer Preinvasive Ade...	Radiomic Algori...	18 Years -	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants	NCT05170282	Hepatoblastoma Hepatic Hemangi...	Radiomic Algori...	0 Months - 12 Months	West China Hospital
Deep Learning Magnetic Resonance Imaging Radiomic Predict Platinum-sensitive in Patients With Epithelial Ovarian Cancer	NCT04511481	Predictive Canc...	Radiomic Algori...	22 Years - 99 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC	NCT04452058	Predictive Canc... Lung Cancer Preinvasive Ade...	Radiomic Algori...	18 Years -	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Deep Learning Magnetic Resonance Imaging Radiomic Predict Platinum-sensitive in Patients With Epithelial Ovarian Cancer	NCT04511481	Predictive Canc...	Radiomic Algori...	22 Years - 99 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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