Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.
Peiyan WuYan JiangHanshuo XingWenbo SongXin-Wu CuiXing-Long WuGuoping XuPublished in: Physics in medicine and biology (2023)
Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC). 
Methods: The clinical and ultrasound (US) imaging data, including brightness mode (B-mode) and color Doppler flow imaging (CDFI), of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics (ROC) and the decision curve analysis (DCA).
Results: The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the primary cohort (PC), validation cohort (VC), test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.
Conclusion: The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, US imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients.Our code is available publicly in the repository at https://github.com/wupeiyan/MDLRN.
Keyphrases
- deep learning
- lymph node metastasis
- high resolution
- papillary thyroid
- patients undergoing
- artificial intelligence
- magnetic resonance imaging
- breast cancer risk
- end stage renal disease
- newly diagnosed
- convolutional neural network
- contrast enhanced
- healthcare
- ejection fraction
- electronic health record
- big data
- prognostic factors
- pregnant women
- type diabetes
- metabolic syndrome
- dna methylation
- computed tomography
- genome wide
- young adults
- squamous cell