Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study

Abstract

Objectives: To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC). Methods and materials: A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness. Results: The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71–0.80), an internal validation C-index of 0.73 (95% CI, 0.69–0.78) and an external validation 0.71 (95% CI,0.66–0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model. Conclusions: The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately. Advances in knowledge: The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics

“…Consistent with previous studies [ 14 , 34 – 36 ], we found that TNBC patients with more metastatic axillary lymph nodes had a worse prognosis. This is expected because the number of positive lymph nodes determines the pathological stage of breast cancer and is important predictor of survival outcomes [ 37 ].…”

“…Among these patients, those with high levels of stromal tumor-infiltrating lymphocytes in the early stage had excellent survival outcomes; thus, a subset of TNBC patients could be spare adjuvant chemotherapy [32,33]. Consistent with previous studies [14,[34][35][36], we found that TNBC patients with more metastatic axillary lymph nodes had a worse prognosis. This is expected because the number of positive lymph nodes determines the pathological stage of breast cancer and is important predictor of survival outcomes [37].…”

Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets

“…Wavelet features have been previously mentioned in the response prediction of colorectal cancer [ 31 ], but their application in breast cancer treatment response remain unexplored., which is worthy of further investigation. Moreover, the addition of clinicopathological data has been reported to increase the predictive ability compared to that of the model alone [ 32 ], but external validation has failed to confirm the superiority of the radiomic model over the clinical model [ 29 ]. In our study, we also failed to show a significant improvement in pCR prediction with the addition of clinical factors.…”

Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

“…The nomogram had superior predictive power to the clinicopathological marrow and the number-based prediction of metastatic lymph nodes. The authors conclude that the addition of clinicopathological data to the radiomic model increases the power of prediction compared to any of the independently evaluated models [72].…”

“…Radiomics for TNBC: the table includes the imaging method chosen for radiomic analysis, the type, the number of features used in creating the model including the radiomic score and nomograms, study objectives, authors and year of publication[58,[62][63][64][65][66][67][68][69][70][71][72][73][74].…”