Multiparametric <scp>MR</scp> Imaging Radiomics Signatures for Assessing the Recurrence Risk of <scp>ER</scp>+/<scp>HER2</scp>− Breast Cancer Quantified With 21‐Gene Recurrence Score

Chen, Yang; Tang, Wei; Liu, Wei; Li, Ruimin; Wang, Qifeng; Shen, Xigang; Gong, Jing; Gu, Yajia; Weijun, Peng

doi:10.1002/jmri.28547

Cited by 5 publications

(4 citation statements)

References 40 publications

(94 reference statements)

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“…Eun et al (2021) demonstrated the superior diagnostic performance of RF model-based texture analysis over conventional MRI and clinicopathological features in predicting breast carcinomatosis recurrence after NAC(AUC: 0.94 vs. 0.83, P<0.05). Chen et al (2022) dictors, which also successfully predicted DFS in patients with invasive breast cancer.…”

Section: Prediction Of Recurrence and Survival Outcomesmentioning

confidence: 89%

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

2023

DSI

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Radiomics is an emerging analytical approach in the medical field that extracts high-throughput quantitative features from multiple imaging data and builds models for cancer diagnosis, prognosis, and treatment by machine learning or deep learning. Radiomics allows radiologists to obtain a more complete picture of the tumor in a noninvasive way than by reading radiographs. Radiogenomics incorporates genomics on top of radiomics to analyze the potential relationshipbetween imaging features and tumor genetic status, enabling biological profiling of the causes of tumor heterogeneity, and its development of biomarkers will be of great help for personalized treatment. Breast cancer is the most prevalent cancer among women worldwide today, and this survey aims to summarize the progress on radiomics and radiogenomics, their applications in breast cancer, and discuss the issues that need to be addressed before radiomics and radiogenomics can be used in clinic. From the literature, it can be concluded that radiomics and radiogenomics have a high potential for differentiating malignant and benign breast lesions to assess breast cancer types and lymph node status, as well as to predict neoadjuvant chemotherapy response, risk of recurrence and survival outcomes, especially in the context of the rapid development of artificial intelligence technologies, promising early realization of precision medicine.

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Section: Prediction Of Recurrence and Survival Outcomesmentioning

confidence: 89%

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

2023

DSI

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“…Based on informal consensus, the panel recommended that oncologists offer chemoendocrine therapy to patients with recurrence scores of 26 to 30 (9). Only one study adopting this cutoff was recently published in literature for assessing ER + / HER2 − breast cancer patients' 21-gene RS using a multiparametric MRI-based radiomics model [49]. They obtained an AUC of 0.82 from DCE of the tumor that improved to 0.92 when adding DWI and T2-weighted images.…”

Section: Discussionmentioning

confidence: 99%

“…In detail, they divided high risk and low-intermedium risk based on a RS cut-off of 25 [9]. In this regard, only one study was recently published on the potential correlation between radiomics and RS adopting this cut-off, but it did not assess the role of peritumoral tissues [49].…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients

et al. 2023

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Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 − invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 − breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10−3). When combining “early” and “peak” DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 − breast cancer patients.

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“…However, the predictive performance has varied widely across studies, with the accuracy ranging from 52.5–86.8%. The predictive accuracy has been further improved via the use of clinicopathologic and magnetic resonance imaging (MRI) parameters, with the resulting area under the receiver operating characteristic curve (AUC) for predicting the probability of RS ≥26 ranging from 0.75 to 0.90 ( 9 - 11 ). Ultrasound (US), given its high soft-tissue contrast and sensitivity, is one of the most common modalities used in the diagnosis of breast cancer ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

The added value of ultrasound imaging biomarkers to clinicopathological factors for the prediction of high-risk Oncotype DX recurrence scores in patients with breast cancer

Luo,

Gao,

Niu

et al. 2024

Quant Imaging Med Surg

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Background The Oncotype DX (ODX) recurrence score (RS), a 21-gene assay, has been proven to recognize patients at high risk of recurrence (RS ≥26) who would benefit from chemotherapy. However, it has limited availability and high costs. Our study thus aimed to identify ultrasound (US) imaging biomarkers and develop a prediction model for identifying patients with a high ODX RS. Methods In this retrospective study, consecutive patients with T1–3N0–1M0 breast cancer who were hormone receptor positive and human epidermal growth factor receptor 2 (HER2) negative who had an available ODX RS were reviewed. Patients treated from May 2012 and December 2015 were placed into a training cohort, and those treated from January 2016 to January 2017 were placed in a validation cohort. Clinicopathologic data were collected, and preoperative US scans were analyzed. Univariable and multivariable regression analyses were performed to evaluate the independent predictors for a high-risk of breast cancer in the training cohort, and a nomogram was developed and evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results A total of 363 patients were in the training cohort and 160 in the validation cohort, with the proportion with a high RS (RS 26–100) being 14% and 13.1%, respectively. Echogenic halo, enhanced posterior echo, low level of progesterone receptor (PR), and high Ki-67 index were identified as independent risk factors for high RS (all P values <0.05). The nomogram was constructed based on the combined model, which showed a better discrimination ability than did the clinicopathological model [combined model: AUC =0.95, 95% confidence interval (CI): 0.93–0.97; clinicopathological model: AUC =0.89, 95% CI: 0.86–0.92; P=0.001] and greater clinical benefit according to DCA. Furthermore, the nomogram was found to be effective in the validation cohort (AUC =0.90, 95% CI: 0.84–0.94), especially in patients with stage T1N0M0 disease (AUC =0.91, 95% CI: 0.84–0.95). Conclusions US features may serve as valuable imaging biomarkers for the prediction of high recurrence risk in patients with T1–3N0–1M0 breast cancer and hormone receptor (HR)-positive and HER2-negative status. A nomogram incorporating PR status, Ki-67 index, and US imaging biomarkers showed a good discrimination ability in the early selection of patients at high risk of recurrence, especially in those with stage T1N0M0 disease.

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Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2− Breast Cancer Quantified With 21‐Gene Recurrence Score

Cited by 5 publications

References 40 publications

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

Progress on radiomics and radiogenomics and their applications in breast cancer: A survey

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients

The added value of ultrasound imaging biomarkers to clinicopathological factors for the prediction of high-risk Oncotype DX recurrence scores in patients with breast cancer

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