Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Carcinoma, Non-Small-Cell Lung

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital 1 (Tongji Medical College Affiliated Union Hospital)

patients who were diagnosed with non-small cell carcinoma and undergo surgery after neoadjuvant chemoimmunotherapy treatment at hospital (Zhengzhou University First Affiliated Hospital, Yichang Central Hospital, Anyang Cancer Hospital)

The high-throughput extraction of large amounts of quantitative image features from medical images

No interventions

This study presents the development and validation of an artificial intelligence (AI) prediction system that utilizes pre-neoadjuvant immunotherapy plain scans and enhanced multimodal CT scans to extract deep learning features. The aim is to predict the occurrence of pathological complete response in non-small cell lung cancer patients undergoing neoadjuvant immunochemotherapyy.

This study retrospectively obtained non-contrast enhanced and contrast enhanced CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy. at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contract enhanced and contract enhanced CT scans to construct the predictive models (LUNAI-nCT model and LUNAI-eCT model), respectively. After feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to generate saliency heatmaps.

Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

A Artificial Intelligence Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

several metrics were calculated, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Wuhan Union Hospital, China

CT-based deep learning model for pure-solid nodules classifications

CT-based deep learning model

Chang Chen, MD, PhD

Minjie Ma, Dr

Yongxiang Song, Dr

Bentong Yu, Dr

Minglei Yang, Dr

The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.

Deep Learning Model for Pure Solid Nodules Classification

Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study

Area under the curve of the receiver operating characteristic

Ratio of the number of correctly classified samples to the total number of samples

The probability of detecting a positive test in the population with the gold standard for disease (positive)

Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy	NCT06285058	Deep Learning M... Pathological Co... Non-small Cell ... Neoadjuvant Che...	No intervention...	18 Years - 80 Years	Wuhan Union Hospital, China
Deep Learning Model for Pure Solid Nodules Classification	NCT05542992	Lung Cancer	CT-based deep l...	18 Years - 75 Years	Shanghai Pulmonary Hospital, Shanghai, China
Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy	NCT06285058	Deep Learning M... Pathological Co... Non-small Cell ... Neoadjuvant Che...	No intervention...	18 Years - 80 Years	Wuhan Union Hospital, China
Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy	NCT06285058	Deep Learning M... Pathological Co... Non-small Cell ... Neoadjuvant Che...	No intervention...	18 Years - 80 Years	Wuhan Union Hospital, China

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