Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based
Jianli Zhao1,2, Zhixian Sun1,2, Yunfang Yu3,4
1Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Background:
Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).
Methods:
Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis.
Results:
Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype.
Conclusions:
This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.
Insights
A new radiomics model accurately predicts which advanced breast cancer patients will benefit from immune checkpoint inhibitors (ICIs). This AI-driven approach using CT scans offers a non-invasive alternative to traditional biomarkers for treatment selection.
Area of Science:
- Radiology
- Oncology
- Artificial Intelligence
Background:
- Immune checkpoint inhibitors (ICIs) are a breakthrough in cancer treatment, but identifying suitable patients remains challenging.
- Current biomarkers for ICI efficacy prediction rely on pathological slides and have limited accuracy.
- There is a need for non-invasive methods to predict response to ICIs in advanced breast cancer (ABC).
Purpose of the Study:
- To develop and validate a radiomics model for predicting ICI response in patients with advanced breast cancer (ABC).
- To compare the predictive performance of the radiomics model against traditional clinical models.
Main Methods:
- A cohort of 240 patients with ABC treated with ICIs was retrospectively analyzed.
- Pretreatment contrast-enhanced CT (CECT) images and clinicopathological data were used.
- Radiomic features were extracted, and a multilayer perceptron model was constructed, integrated with clinical data using multivariable logistic regression.
Main Results:
- The radiomics model achieved high predictive accuracy (AUC=0.994 in training, 0.920 in validation), significantly outperforming the clinical model.
- The integrated clinical-radiomics model showed excellent performance (AUC=0.997 in training, 0.961 in validation).
- The radiomics model effectively stratified patients into high- and low-risk groups with significantly different progression-free survival and was robust across subgroups.
Conclusions:
- A novel radiomics model accurately predicts ICI response in advanced breast cancer patients.
- This model offers an innovative and accurate tool for stratifying patients who may benefit from ICIs-based therapies.
- The radiomics approach provides a non-invasive method for optimizing treatment selection in ABC.
