Delta Radiomics Based on Longitudinal Dual-modal Ultrasound Can Early Predict Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Novel MRI-based Hyper-Fused Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer

RATIONALE AND OBJECTIVES

To propose a novel MRI-based hyper-fused radiomic approach to predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC).

MATERIALS AND METHODS

Pretreatment dynamic contrast-enhanced (DCE) MRI and ultra-multi-b-value (UMB) diffusion-weighted imaging (DWI) data were acquired in BC patients who received NAT followed by surgery at two centers. Hyper-fused radiomic features (RFs) and conventional RFs were extracted from DCE-MRI or UMB-DWI. After feature selection, the following models were built using logistic regression and the retained RFs: hyper-fused model, conventional model, and compound model that integrates the hyper-fused and conventional RFs. The output probability of each model was used to generate a radiomic signature. The model's performance was quantified by the area under the receiver-operating characteristic curve (AUC). Multivariable logistic regression was used to identify variables (clinicopathological variables and the generated radiomic signatures) associated with pCR.

RESULTS

The training/external test set (center 1/2) included 547/295 women. The hyper-fused models (AUCs=0.81-0.85) outperformed (p<0.05) the conventional models (AUCs=0.74-0.80) in predicting pCR. The compound models (AUCs=0.88-0.93) outperformed (p<0.05) the hyper-fused models and conventional models for pCR prediction. The hyper-fused radiomic signatures (odds ratios=5.70-12.98; p<0.05) and compound radiomic signatures (odds ratios=1.57-7.71; p<0.05) were independently associated with pCR. These are true for the training and external test sets.

CONCLUSION

The hyper-fused radiomic approach had significantly better performance for predicting pCR to NAT than the conventional radiomic approach, and the hyper-fused RFs provided incremental discrimination of pCR beyond the conventional RFs. The generated hyper-fused radiomic signatures were independent predictors of pCR.

Radiomics of Multimodal Ultrasound for Early Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To construct and validate a clinical-radiomics model based on radiomics features extracted from two-stage multimodal ultrasound and clinicopathologic information for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients treated with NAC.

MATERIALS AND METHODS

Consecutive women with biopsy-proven breast cancer undergoing multimodal US pretreatment and after two cycles of NAC and followed by surgery between January 2014 and November 2023 were retrospectively collected for clinical-radiomics model construction (n = 274) and retrospective test (n = 134). The predictive performance of it was further tested in a subsequent prospective internal test set recruited between January 2024 to July 2024 (n = 76). Finally, a total of 484 patients were enrolled. The clinical-radiomics model predictive performance was compared with radiomics model, clinical model and radiologists' visual assessment by area under the receiver operating characteristic curve (AUC) analysis and DeLong test.

RESULTS

The proposed clinical-radiomics model obtained the AUC values of 0.92 (95%CI: 0.88, 0.94) and 0.85 (95%CI: 0.79, 0.89) in retrospective and prospective test sets, respectively, which were significantly higher than that those of the radiomics model (AUCs: 0.75-0.85), clinical model (AUCs: 0.68-0.72) and radiologists' visual assessments (AUCs:0.59-0.68) (all p < 0.05). In addition, the predictive efficacy of the radiologists was improved under the assistance of the clinical-radiomics model significantly.

CONCLUSION

The clinical-radiomics model developed in this study, which integrated clinicopathologic information and two-stage multimodal ultrasound features, was able to early predict pCR to NAC in breast cancer patients with favorable predictive effectiveness.

A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting axillary lymph node (ALN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients and its potential to assist radiologists in diagnosis.

METHODS

Two medical centers retrospectively recruited 535 node-positive breast cancer patients who had undergone NAC. Center 1 included 288 patients in the training cohort and 123 patients in the internal validation cohort, while center 2 enrolled 124 patients for the external validation cohort. Five DL models (ResNet 34, ResNet 50, VGG19, GoogLeNet, and DenseNet 121) were trained on pre- and post-NAC US images, and the best model was chosen. A US-based DL nomogram was constructed using DL predictive probabilities and clinicopathological characteristics. Furthermore, the performances of radiologists were compared with and without the assistance of the nomogram.

RESULT

ResNet 50 performed best among all DL models, achieving areas under the curve (AUCs) of 0.837 and 0.850 in the internal and external validation cohorts, respectively. The US-based DL nomogram demonstrated strong predictive ability for ALN status post-NAC, with AUCs of 0.890 and 0.870 in the internal and external validation cohorts, respectively, outperforming both the clinical model and the DL model (p all < 0.05, except p = 0.19 for DL model in external validation cohort). Moreover, the nomogram significantly improved radiologists' diagnostic ability.

CONCLUSION

The US-based DL nomogram is promising for predicting ALN status post-NAC and could assist radiologists for better diagnostic performance.

Delta Radiomics Based on MRI for Predicting Axillary Lymph Node Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients

PURPOSE

To develop and test a radiomics nomogram based on magnetic resonance imaging (MRI) and clinicopathological factors for predicting the axillary pathologic complete response (apCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients with axillary lymph node (ALN) metastases.

MATERIALS AND METHODS

A total of 319 patients who underwent MRI examination and received NAC treatment were enrolled from two centers, and the presence of ALN metastasis was confirmed by biopsy pathology before NAC. The radiomics features were extracted from regions of interest of ALNs before (pre-radiomics) and after (post-radiomics) NAC. The difference of features before and after NAC, named delta radiomics, was calculated. The variance threshold, selectKbest and least absolute shrinkage and selection operator algorithm were used to select radiomics features. Radscore was calculated by a linear combination of selected features, weighted by their respective coefficients. The univariate and multivariate logistic regression was used to select the clinicopathological factors and radscores, and a radiomics nomogram was built by multivariable logistic regression analysis. The performance of the nomogram was evaluated by the area under the receiver operator characteristic curve (AUC), decision curve analysis (DCA) and calibration curves. Furthermore, to explore the biological basis of radiomics nomogram, 16 patients with RNA-sequence data were included for genetic analysis.

RESULTS

The radiomics nomogram was constructed by two radscores (post- and delta- radscores) and one clinicopathological factor (progesterone hormone, PR), and showed powerful predictive performance in both internal and external test sets, with AUCs of 0.894 (95% confidence interval [CI], 0.877-0.959) and 0.903 (95% CI, 0.801-0.986), respectively. The calibration curves and DCA showed favorable consistency and clinical utility. With the assistance of nomogram, the rate of unnecessary ALND would be reduced from 60.42% to 21.88%, and the rate of final benefit rate would be increased from 39.58% to 70.83%. Moreover, genetic analysis revealed that high apCR prediction scores were associated with the upregulation of immune-mediated genes and pathways.

CONCLUSION

The radiomics nomogram showed great performance in predicting apCR after NAC for breast cancer patients, which could help clinicians to identify patients with apCR and avoid unnecessary axillary lymph node dissection.

Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography

PURPOSE

To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement.

METHODS

This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed.

RESULTS

Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X2 = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%.

CONCLUSIONS

SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Additionally, we employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm.

MATERIALS AND METHODS

A total of 243 patients who underwent NAC were retrospectively included between October 2014 and July 2022. The DLRN integrated clinical characteristics as well as radiomics and deep transfer learning features extracted from ultrasound (US) images. The diagnostic performance of DLRN was evaluated by constructing ROC curves, and the clinical usefulness of models was assessed using decision curve analysis (DCA). A survival model was developed to validate the effectiveness of the fusion algorithm.

RESULTS

In the training cohort, the DLRN yielded area under the receiver operating characteristic curve values of 0.984 and 0.985 for the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency indices (C-index) of the nomogram were 0.761 and 0.731, respectively, in the training and test cohorts. The Kaplan-Meier survival curves showed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group (P < 0.05).

CONCLUSION

The US-based DLRN model could hold promise as clinical guidance for predicting the status of tumors and LNM after NAC in patients with breast cancer. This fusion model can also predict the prognosis of patients, which could help clinicians make better clinical decisions.