Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer

Abstract

Background:Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumours, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumours' response to chemotherapy and provides important prognostic information. There are currently no clearly de ned clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. Methods:The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): rst two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on Deep Learning (DL). Clinical parameters were included to build a nal prognostic model. Results:The best performing models were based on space-resolved and deep learning approaches. Spaceresolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Conclusions:Radiomics features extracted from diagnostic CT augments the predictive ability of pathological complete response when combined with clinical features. The novel space-resolved radiomics and deep learning radiomics approaches outperformed conventional radiomics techniques.

Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study

“…Multivariable logistic regression analysis revealed that Ki-67 index was a significant predictor of pCR status in the TC (Supplementary Figure S7A), and it was used to construct the clinical model as a baseline. Consistent with previous studies, 32 , 33 the performance of clinical models in predicting pCR was unsatisfactory, with AUC values of 0.588 (95% confidence interval (CI): 0.521, 0.655), 0.524 (95%CI: 0.425, 0.622), and 0.540 (95%CI: 0.437, 0.643) in the three cohorts. (Supplementary Table S3).…”

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

“…Some difficulties have been recently overcome thanks to deep learning, such as time-consuming manual labeling, inconsistent DCE-MRI protocols, etc. Furthermore, fully automatic segmentation is not restricted to intratumoral features, breast tissue [ 40 ] and peritumoral [ 41 ] can also give an early prediction. This is the direction of our further efforts.…”

Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis

“…Different from the relatively mature static image radiomics studies, there are technical difficulties in applying static image radiomics analysis methods to video analysis with a length of more than 1 min (4). Given that the practice of medicine is constantly evolving in response to new technology, there is interest in using the latest imaging methods to obtain data for radiomics learning (5,6).…”