Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.

BACKGROUND: To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS: One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION: This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

BACKGROUND: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE: Retrospective. POPULATION: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Model-Free and Model-based Parameters Derived From CAIPIRINHA-Dixon-TWIST-VIBE DCE-MRI: Associations With Prognostic Factors and Molecular Subtypes of Invasive Ductal Breast Cancer.

BACKGROUND: CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) dynamic contrast-enhanced MRI (DCE-MRI) can be used to characterize breast cancer. However, the influence of the clinicopathologic factors and molecular subtypes of invasive breast carcinoma (IDC) on the model-free and model-based parameters has not been investigated. PURPOSE: To compare model-free and model-based parameters of CDTV DCE-MRI with both clinicopathologic factors and molecular subtypes of IDC. STUDY TYPE: Prospective. POPULATION: A total of 152 patients (mean age, 52 years) with IDC including 42 luminal A, 64 luminal B, 22 human epidermal growth factor receptor-2 (HER2) positive, and 24 triple-negative subtypes. FIELD STRENGTH/SEQUENCE: A 3 T; turbo-FLASH, Dixon VIBE, and CDTV. ASSESSMENT: Model-free parameters (initial enhancement rate [IER] and maximum slope [MS]) were estimated from the time-intensity curve. The mean, minimum, maximum, and range between the minimum and maximum values of inline model-based parameters (Ktrans , kep , and ve ) were measured to assess intratumoral heterogeneity of IDC lesions. STATISTICAL TESTS: Student's t tests, Mann-Whitney U tests, Kruskal-Wallis tests, post hoc Steel-Dwass tests, and receiver operating characteristic (ROC) curves. P < 0.05 was considered significant. RESULTS: No significant differences in IER and MS values were seen among the clinicopathologic factors and molecular subtypes (Bonferroni-corrected P = 0.011-0.862, P = 0.145-0.601, respectively). The minimum kep values in HER2-positive IDC were significantly lower than those in HER2-negative IDC. The mean and range kep values were independent predictors for distinguishing the high (grade 3) and low (grade 1 or 2) nuclear grade groups according to multivariable analyses. The post hoc test showed that the kep minimum and kep range values were significantly different between luminal A and HER2-positive tumor subtypes, yielding an area-under-the-curve of 0.820. DATA CONCLUSION: Compared with the model-free parameters, inline kep related model-based parameters on CDTV DCE-MRI can be applied as a feasible tool to differentiate luminal A from HER2-positive breast cancers. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

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

BACKGROUND: While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability. PURPOSE: To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS. STUDY TYPE: Retrospective. SUBJECTS: A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n = 106) and validation (n = 45) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5 T or 3 T. ASSESSMENT: A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant. RESULTS: The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC = 0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC = 0.92 and 0.64, respectively) and validation cohorts (AUC = 0.78 and 0.62, respectively). DATA CONCLUSION: The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Diffusion-Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High-Grade Serous Ovarian Carcinoma.

BACKGROUND: Preoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded. PURPOSE: To investigate the value of diffusion-weighted MRI (DW-MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients. STUDY TYPE: Prospective. SUBJECTS: A total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years). FIELDS STRENGTH/SEQUENCE: A 3.0 T, readout-segmented echo-planar DWI. ASSESSMENT: The MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI-PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI-PCI alone; and a fusion model combining the clinical-morphological information and DWI-PCI. STATISTICAL TESTS: Multivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant. RESULTS: Sixty-seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI-PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA-125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI-PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI. DATA CONCLUSIONS: The combination of two clinical factors, MRI morphological characteristics and DWI-PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

OBJECTIVES: To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS: A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS: The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS: The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT: An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS: • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.

OBJECTIVE: This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS: In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS: Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS: The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS: • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study.

OBJECTIVE: To construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography. METHODS: A total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists. RESULTS: The nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram. CONCLUSION: The nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities. KEY POINTS: • Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions. • The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.

Using a CT-based scale to evaluate disease extension and the resectability of locally advanced thyroid cancer.

OBJECTIVES: To establish a computed tomography (CT)-based scale to evaluate the resectability of locally advanced thyroid cancer. METHODS: This twin-centre retrospective study included 95 locally advanced thyroid cancer patients from the 1st centre as the training cohort and 31 patients from the 2nd centre as the testing cohort, who were categorised into the resectable and unresectable groups. Three radiologists scored the CT scans of each patient by evaluating the extension to the recurrent laryngeal nerve (RLN), trachea, oesophagus, artery, vein, soft tissue, and larynx. A 14-score scale (including all comprised structures) and a 12-score scale (excluding larynx) were developed. Receiver-operating characteristic (ROC) analysis was used to evaluate the performance of the scales. Stratified fivefold cross-validation and external verification were used to validate the scale. RESULTS: In the training cohort, compromised RLN (p < 0.001), trachea (p = 0.001), oesophagus (p = 0.002), artery (p < 0.001), vein (p = 0.005), and soft tissue (p < 0.001) were predictors for unresectability, while compromised larynx (p = 0.283) was not. The 12-score scale (AUC = 0.882, 95%CI: 0.812-0.952) was not inferior to the 14-score scale (AUC = 0.891, 95%CI: 0.823-0.960). In subgroup analysis, the AUCs of the 12-score scale were 0.826 for treatment-naïve patients and 0.976 for patients with prior surgery. The 12-score scale was further validated with a fivefold cross-validation analysis, with an overall accuracy of 78.9-89.4%. Finally, external validation using the testing cohort showed an AUC of 0.875. CONCLUSIONS: The researchers built a CT-based 12-score scale to evaluate the resectability of locally advanced thyroid cancer. Validation with a larger sample size is required to confirm the efficacy of the scale. CLINICAL RELEVANCE STATEMENT: This 12-score CT scale would help clinicians evaluate the resectability of locally advanced thyroid cancer. KEY POINTS: • The researchers built a 12-score CT scale (including recurrent laryngeal nerve, trachea, oesophagus, artery, vein, and soft tissue) to evaluate the resectability of locally advanced thyroid cancer. • This scale has the potential to help clinicians make treatment plans for locally advanced thyroid cancer.

Association of Clinical Factors and Degree of Early Background Parenchymal Enhancement on Contrast-Enhanced Mammography.

BACKGROUND. Background parenchymal enhancement (BPE) may impact contrast-enhanced mammography (CEM) interpretation, although factors influencing the degree of BPE on CEM are poorly understood. OBJECTIVE. The purpose of our study was to evaluate relationships between clinical factors and the degree of early BPE on CEM. METHODS. This retrospective study included 207 patients (median age, 46 years) who underwent CEM between April 2020 and September 2021. Two radiologists independently assessed the degree of BPE on CEM as minimal, mild, moderate, or marked on the basis of two criteria (criterion 1, using the first of four obtained views; criterion 2, using the first two of four obtained views). The radiologists reached consensus for breast density on CEM. The EMR was reviewed for clinical factors. Radiologists' agreement for degree of BPE was assessed using weighted kappa coefficients. Univariable and multivariable analyses were performed to assess relationships between clinical factors and degree of BPE, treating readers' independent assessments as repeated measurements. RESULTS. Interreader agreement for degree of BPE, expressed as kappa, was 0.80 for both criteria. For both criteria, univariable analyses found degree of BPE to be negatively associated with age (both OR = 0.94), personal history of breast cancer (OR = 0.22-0.30), history of chemotherapy (OR = 0.18-0.21), history of radiation therapy (OR = 0.20-0.21), perimenopausal status (OR = 0.22-0.34), and postmenopausal status (OR = 0.10-0.11) and to be positively associated with dense breasts (OR = 4.13-4.26) and premenopausal status with irregular menstrual cycles (OR = 7.94-14.02). Among premenopausal patients with regular menstrual cycles, degree of BPE was lowest (using postmenopausal patients as reference) for patients in menstrual cycle days 8-14 (OR = 2.56-3.30). In multivariable analysis for both criteria, the only independent predictors of degree of BPE related to menstrual status and time of menstrual cycle (e.g., using premenopausal patients in days 1-7 as reference: OR = 0.21 for both criteria for premenopausal patients in days 8-14 and OR = 0.03-0.04 for postmenopausal patients). CONCLUSION. Clinical factors, including history of breast cancer or breast cancer treatment, breast density, menstrual status, and time of menstrual cycle, are associated with degree of early BPE on CEM. In premenopausal patients, the degree of BPE is lowest on days 8-14 of the menstrual cycle. CLINICAL IMPACT. Given the potential impact of BPE on diagnostic performance, the findings have implications for CEM scheduling and interpretation.

Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor-Positive Breast Cancer: A Multicohort Study.

Background Radiogenomics explores the association between imaging features and genomic assays to uncover relevant prognostic features; however, the prognostic implications of the derived signatures remain unclear. Purpose To identify preoperative radiogenomic signatures of estrogen receptor-positive breast cancer associated with the Oncotype DX recurrence score (RS) and to evaluate whether they are biomarkers for survival and responses to neoadjuvant chemotherapy (NACT). Materials and Methods In this retrospective multicohort study, three data sets were analyzed. The radiogenomic development data set, with preoperative dynamic contrast-enhanced MRI and RS data obtained between January 2016 and October 2019 was used to identify radiogenomic signatures. Prognostic implications of the imaging signatures were assessed by measuring overall survival and recurrence-free survival in the prognostic assessment data set using a multivariable Cox proportional hazards model. The therapeutic implication of the radiogenomic signatures was evaluated by determining their ability to predict the response to NACT using the treatment assessment data set obtained between August 2015 and March 2019. Prediction performance was estimated by using the area under the receiver operating characteristic curve (AUC). Results The final cohorts included a radiogenomic development data set with 130 women (mean age, 52 years ± 10 [standard deviation]), a prognostic assessment data set with 116 women (mean age, 48 years ± 9), and a treatment assessment data set with 135 women (mean age, 50 years ± 11). Radiogenomic signatures (n = 11) of texture and morphologic and statistical features were identified to generate the predicted RS (R2 = 0.33, P < .001). A predicted RS greater than 29.9 was associated with poor overall and recurrence-free survival (P = .001 and P = .007, respectively); predicted RS was greater in women with a good NACT response (30.51 ± 6.92 vs 27.35 ± 4.04 [responders vs nonresponders], P = .001). By combining the predicted RS and complementary features, the model achieved improved performance in prediction of the NACT response (AUC, 0.85; P < .001). Conclusion Radiogenomic signatures associated with genomic assays provide markers of prognosis and treatment in estrogen receptor-positive breast cancer. © RSNA, 2021 Online supplemental material is available for this article.

Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. METHODS: We retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures. RESULTS: We selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638-0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8+ T cells, follicular helper T cells and memory B cells, was found in high Rad-TILs tumors. CONCLUSIONS: Our study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC.

A Multiomics Signature Highlights Alterations Underlying Homologous Recombination Deficiency in Triple-Negative Breast Cancer.

BACKGROUND: Homologous recombination (HR) is a key pathway in DNA double-strand damage repair. HR deficiency (HRD) occurs more commonly in triple-negative breast cancers (TNBCs) than in other breast cancer subtypes. Several clinical trials have demonstrated the value of HRD in stratifying breast cancer patients into distinct groups based on their responses to poly(ADP ribose) polymerase inhibitors and chemotherapy. METHODS: We retrospectively collected TNBC samples to establish a multiomics cohort (n = 343) and explored the biological and phenotypic mechanisms underlying the better prognosis of patients with high HRD scores. Gene set enrichment analysis was conducted to elucidate the underlying pathways in patients with low HRD scores, and a radiomics model was established to predict the HRD score via a noninvasive method. RESULTS: Multivariable Cox analysis revealed the independent prognostic value of a low HRD score (hazard ratio 2.20, 95% confidence interval 1.05-4.59; p = 0.04). Furthermore, amino acid and lipid metabolism pathways were highly enriched in tumors from patients with low HRD scores, which was also demonstrated by differential abundant metabolite analysis. A noninvasive radiomics method was developed to predict the HRD status and it performed well in the independent validation cohort (support vector machine model: area under the curve [AUC] 0.739, sensitivity 0.571, and specificity 0.824; logistic regression model: AUC 0.695, sensitivity 0.571, and specificity 0.882). CONCLUSIONS: We revealed the prognostic value of the HRD score, predicted the HRD status with noninvasive radiomics features, and preliminarily explored druggable targets for TNBC patients with low HRD scores.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm).

OBJECTIVES: To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS: This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS: For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS: Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS: • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.

OBJECTIVE: To conduct perilesional region radiomics analysis of contrast-enhanced mammography (CEM) images to differentiate benign and malignant breast lesions. METHODS AND MATERIALS: This retrospective study included patients who underwent CEM from November 2017 to February 2020. Lesion contours were manually delineated. Perilesional regions were automatically obtained. Seven regions of interest (ROIs) were obtained for each lesion, including the lesion ROI, annular perilesional ROIs (1 mm, 3 mm, 5 mm), and lesion + perilesional ROIs (1 mm, 3 mm, 5 mm). Overall, 4,098 radiomics features were extracted from each ROI. Datasets were divided into training and testing sets (1:1). Seven classification models using features from the seven ROIs were constructed using LASSO regression. Model performance was assessed by the AUC with 95% CI. RESULTS: Overall, 190 women with 223 breast lesions (101 benign; 122 malignant) were enrolled. In the testing set, the annular perilesional ROI of 3-mm model showed the highest AUC of 0.930 (95% CI: 0.882-0.977), followed by the annular perilesional ROI of 1 mm model (AUC = 0.929; 95% CI: 0.881-0.978) and the lesion ROI model (AUC = 0.909; 95% CI: 0.857-0.961). A new model was generated by combining the predicted probabilities of the lesion ROI and annular perilesional ROI of 3-mm models, which achieved a higher AUC in the testing set (AUC = 0.940). CONCLUSIONS: Annular perilesional radiomics analysis of CEM images is useful for diagnosing breast cancers. Adding annular perilesional information to the radiomics model built on the lesion information may improve the diagnostic performance. KEY POINTS: • Radiomics analysis of the annular perilesional region of 3 mm in CEM images may provide valuable information for the differential diagnosis of benign and malignant breast lesions. • The radiomics information from the lesion region and the annular perilesional region may be complementary. Combining the predicted probabilities of the models constructed by the features from the two regions may improve the diagnostic performance of radiomics models.

Differentiation of Prostate Cancer and Stromal Hyperplasia in the Transition Zone With Monoexponential, Stretched-Exponential Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging in a Reduced Number of b Values: Correlation With Whole-Mount Pathology.

OBJECTIVES: The aims of the study were to explore the feasibility of generating a monoexponential model (MEM), stretched-exponential model (SEM) based diffusion-weighted imaging (DWI), and diffusion kurtosis imaging (DKI) by applying the same set of reduced b values and to compare their effectiveness in distinguishing prostate cancer from stromal hyperplasia (SH) in the transition zone (TZ) area. METHODS: An analysis of 75 patients who underwent preoperative DWI ( b values of 0, 700, 1400, 2000 s/mm 2 ) was performed. All lesions were localized on magnetic resonance images according to whole-mount histopathological correlations. The apparent diffusion coefficient (ADC), water molecular diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), mean diffusivity (MD), and mean kurtosis (MK) values were calculated and compared between the TZ cancer and SH groups. Receiver operating characteristic analysis and areas under the receiver operating characteristic curve (AUCs) were carried out for all parameters. RESULTS: Compared with the SH group, the ADC, DDC, α, and MD values of the TZ cancer group were significantly reduced, while the MK value was significantly increased (all P < 0.05). The AUCs of the ADC, DDC, α, MD, and MK were 0.828, 0.801, 0.813, 0.822, and 0.882, respectively. The AUC of MK was significantly higher than that of the other parameters (all P < 0.05). CONCLUSIONS: When using the reduced b -value set, all parameters from MEM, SEM, based DWI, and DKI can effectively distinguish TZ cancer from SH. Among them, DKI demonstrated potential clinical superiority over the others in TZ cancer diagnosis.

Improved Readout-Segmented Echo-Planner Diffusion-Weighted Magnetic Resonance Imaging of Nasopharyngeal Carcinoma Using Simultaneous Multislice Acquisitions at 3 T.

PURPOSE: This study systematically compared the images from readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI [RS-DWI]) and simultaneous multislice accelerated RESOLVE-DWI (SMS-RS-DWI) in patients with nasopharyngeal carcinoma (NPC) in qualitative and quantitative aspects. METHOD: Forty-four patients with NPC were included. The RS-DWI and prototypic SMS-RS-DWI sequences were performed on all patients. Images were qualitatively evaluated by 4 independent radiologists using a 5-point Likert scale. For quantitative evaluation, the maximum and minimum diameters and the maximum tumor areas were determined for both DWI sequences and compared with the T2-weighted imaging (T2WI) to evaluate image distortions. The apparent diffusion coefficient was measured in the slice with the maximum tumor profile. RESULTS: The SMS-RS-DWI was superior to RS-DWI with respect to overall image quality (3.40 ± 0.53 vs 2.71 ± 0.48, P < 0.0001) and tumor edge sharpness (3.29 ± 0.65 vs 2.64 ± 0.47, P < 0.0001). Susceptibility artifacts were significantly less severe in SMS-RS-DWI than in RS-DWI (0.85 ± 0.57 vs 1.36 ± 0.57, P < 0.0001). There was no significant overestimation or underestimation of the tumor geometry using the SMS-RS-DWI or RS-DWI compared with T2WI. The quantitative analysis showed a slightly higher agreement for SMS-RS-DWI with T2WI than RS-DWI for maximum diameter, minimum diameter, and maximum tumor area. The apparent diffusion coefficient values showed no significant differences between the 2 DWI techniques ( P > 0.05). CONCLUSIONS: At 3 T, SMS-RS-DWI is a useful technique for diagnosing NPC. It substantially improves different aspects of image quality by providing higher spatial resolution and fewer susceptibility artifacts with more extensive anatomic coverage compared with RS-DWI.

High relaxivity Gd3+-based organic nanoparticles for efficient magnetic resonance angiography.

Contrast-enhanced MR angiography (MRA) is a critical technique for vascular imaging. Nevertheless, the efficacy of MRA is often limited by the low rate of relaxation, short blood-circulation time, and metal ion-released potential long-term toxicity of clinical available Gd-based contrast agents. In this work, we report a facile and efficient strategy to achieve Gd-chelated organic nanoparticles with high relaxivity for T1-weighted MRA imaging. The Gd-chelated PEG-TCPP nanoparticles (GPT NPs) have been engineered composite structured consisting of Gd-chelated TCPP and PEG. The spherical structure of TCPP offers more chemical sites for Gd3+ coordination to improve the relaxivity and avoid leakage of the Gd3+ ions. The synthesized GPT NPs exhibit a high relaxation rate of 35.76 mM- 1 s- 1 at 3.0 T, which is higher than the rates for most reported MR contrast agents. Therefore, GPT NPs can be used for MRA with much stronger vascular signals, longer circulation time, and high-resolution arterial vascular visualization than those using clinical MR contrast agents at the same dose. This work may make the T1 MRI contrast agents for high-resolution angiography possible and offer a new candidate for preclinical and clinical applications of MR vascular imaging and vascular disease diagnosis.