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Background The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. Methods A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1‐weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max‐pooling layers and ended with three dense layers. The pre‐NAC model and post‐NAC model inputted six phases of pre‐NAC and post‐NAC images, respectively. The combined model used 12 channels from six phases of pre‐NAC and six phases of post‐NAC images. All models above included three indexes of molecular type as one additional input channel. Results The training set contained 137 non‐pCR and 107 pCR participants. The validation set contained 33 non‐pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre‐NAC, 0.968 for post‐NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre‐NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post‐NAC model (100% vs. 82.8%, P = 0.033). Conclusion This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre‐NAC and post‐NAC MRI data. The model performed better than using pre‐NAC data only, and also performed better than using post‐NAC data only. Key points Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre‐NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre‐NAC and post‐NAC MRI data.

This study aimed to develop a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative prediction of two distinct histopathological vascular patterns in hepatocellular carcinoma (HCC): vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI). In addition, the study evaluated the prognostic significance of these vascular patterns in predicting postoperative outcomes in patients with HCC. A total of 306 patients with HCC who underwent radical resection at two medical centers were retrospectively included. Patients from Center 1 were randomly assigned to a training cohort and an internal validation cohort at a ratio of 7:3, while patients from Center 2 comprised the external validation cohort. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PP), and delayed phase (DP) DCE-MRI images, including intratumoral, peritumoral, and fused intra-peritumoral regions. Radiomics models were constructed based on these features, and the optimal model was subsequently integrated with clinical variables to establish a combined clinical-radiomics model. For predicting VETC and/or MVI (defined as VM patterns), the combined model achieved area under the curve (AUC) values of 0.857 in the training cohort, 0.761 in the internal validation cohort, and 0.723 in the external validation cohort. Calibration and decision curve analysis (DCA) indicated acceptable calibration performance and potential clinical utility of the combined model. Both pathological VM positivity and model-predicted VM positivity were significantly associated with early recurrence (ER) and shorter disease-free survival (DFS). The clinical-radiomics combined model based on DCE-MRI holds potential value as a noninvasive preoperative approach for evaluating VM patterns and postoperative prognosis in patients with HCC.

Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

Purpose: Dynamic contrast-enhanced magnetic resonance imaging offers noninvasive characterization of the vascular microenvironment and hemodynamics. Stereotactic radiosurgery, or stereotactic body radiation therapy, engages a vascular component of the tumor response which may be detectable using dynamic contrast-enhanced magnetic resonance imaging. The purpose of this study is to examine whether dynamic contrast-enhanced magnetic resonance imaging can be used to predict local tumor recurrence in patients with spinal bone metastases who undergo high-dose radiotherapy with stereotactic radiosurgery. Materials and Methods: We conducted a study of 30 patients with spinal metastases who underwent dynamic contrast-enhanced magnetic resonance imaging before and after radiotherapy. Twenty patients received single-fraction stereotactic radiosurgery (24 Gy), while 10 received hypofractionated stereotactic radiosurgery (3-5 fractions, 27-30 Gy total). Kaplan-Meier analysis was used to estimate the actuarial local recurrence rates. Two perfusion parameters (Ktrans: permeability and Vp: plasma volume) were measured for each metastasis. Percentage change in parameter values from pre- to posttreatment was calculated and compared. Results: At 20-month median follow-up, 5 of the 30 patients had pathological evidence of local recurrence. One- and 3-year actuarial local recurrence rates were 24% and 44% for the hypofractionated stereotactic radiosurgery cohort versus 5% and 16% for the single-fraction stereotactic radiosurgery cohort (P = .20). The average change in Vp and Ktrans for patients without local recurrence versus those with local recurrence was −76% and −66% versus +28% and −14% (P < .01 for both). With a cutoff point of −20%, Vp had a sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 98%, 91%, and 100%, respectively, for the detection of local recurrence following high-dose radiotherapy. Using this definition, dynamic contrast-enhanced magnetic resonance imaging identified local recurrence up to 18 months (mean [standard deviation], 6.6 [6.8] months) earlier than standard magnetic resonance imaging. Conclusions: We demonstrated that changes in perfusion parameters, particularly Vp, after high-dose radiotherapy to spinal bone metastases were predictive of local tumor recurrence. These changes predicted local recurrence on average >6 months earlier than standard imaging did.

Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.

Objectives Previous deep learning studies have not explored the synergistic effects of ROI dimensions (2D/2.5D/3D), peritumoral expansion levels (0–8 mm), and segmentation scenarios (ROI only vs. ROI original). Our study aims to evaluate the performance of multidimensional deep transfer learning models in distinguishing molecular subtypes of breast cancer (luminal vs. nonluminal) using DCE‐MRI. Under two segmentation scenarios, we systematically compare the effects of ROI dimensions and peritumoral expansion levels to optimize multidimensional deep learning models via transfer learning for distinguishing luminal from nonluminal breast cancers in DCE‐MRI‐based analysis. Materials and Methods From October 2020 to October 2023, data from 426 patients with primary invasive breast cancer were retrospectively collected. Patients were divided into three cohorts: (1) training cohort, n = 108, from SYSU Hospital (Zhuhai, China); (2) validation cohort 1, n = 165, from HZ Hospital (Huizhou, China); and (3) validation cohort 2, n = 153, from LY Hospital (Linyi, China). ROIs were delineated, and expansions of 2, 4, 6, and 8 mm beyond the lesion boundary were performed. We assessed the performance of various deep transfer learning models, considering precise segmentation (ROI only and ROI original) and varying peritumoral regions, using ROC curves and decision curve analysis. Results The 2.5D1‐based deep learning model (ROI original, 4 mm expansion) demonstrated optimal performance, achieving an AUC of 0.808 (95% CI 0.715–0.901) in the training cohort, 0.766 (95% CI 0.682–0.850) in validation cohort 1, and 0.799 (95% CI 0.725–0.874) in validation cohort 2. Conclusion The study highlights that the 2.5D1‐based deep learning model utilizing the three principal slices of the minimum bounding box (ROI original) with a 4 mm peritumoral region is effective in distinguishing between luminal and nonluminal breast cancer tumors, serving as a potential diagnostic tool.

The Liver Imaging Reporting and Data System (LI-RADS) is a widely utilized tool for classifying liver lesions, particularly in patients at risk for hepatocellular carcinoma (HCC). This study aims to assess the efficacy of LI-RADS in distinguishing between HCC and benign liver nodules by leveraging dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and apparent diffusion coefficient (ADC) values derived from MRI. Between October 2023 and March 2024, 43 patients with suspected HCC underwent MRI evaluation, including DCE-MRI and DWI sequences. The diagnostic performance of various MRI sequences was analyzed, focusing on their ability to differentiate HCC from benign lesions. The diagnostic efficacy of DCE-MRI and ADC in differentiation was evaluated using statistical analyses, such as t-tests and receiver operating characteristic (ROC) curve analysis. SPSS VER 16 was used to analyze the collected data. The study findings reveal that the DCE-MRI arterial phase demonstrated perfect diagnostic accuracy with an area under the curve (AUC) of 1.00, achieving 100% sensitivity and specificity. T2-weighted imaging also exhibited diagnostic solid performance, with an AUC of 0.801, while ADC values from DWI sequences showed limited efficacy in differentiating HCC from benign lesions (AUC=0.512). These findings indicate that DCE-MRI significantly enhances the accuracy of LI-RADS in classifying HCC versus benign liver nodules. This study highlights the importance of incorporating advanced imaging features into LI-RADS to improve the diagnostic precision of liver lesion evaluation in clinical practice.  

Introduction Studies have recognized that the loss of the blood–brain barrier (BBB) integrity is a major structural biomarker where neurodegenerative disease potentially begins. Using a combination of high‐quality neuroimaging techniques, we investigated potential subtle differences in BBB permeability in mid‐age healthy people, comparing carriers of the apolipoprotein E epsilon‐4 (APOEε4) genotype, the biggest risk factor for late onset, non‐familial AD (LOAD) with APOEε3 carriers, the population norm. Methods Forty‐one cognitively healthy mid‐age participants (42–59) were genotyped and pseudo‐randomly selected to participate in the study by a third party. Blind to genotype, all participants had a structural brain scan acquisition including gadolinium‐based dynamic contrast‐enhanced magnetic resonance imaging acquired using a T1‐weighted 3D vibe sequence. A B1 map and T1 map were acquired as part of the multi‐parametric mapping acquisition. Results Non‐significant, but subtle differences in blood–brain barrier permeability were identified between healthy mid‐age APOEε4 and APOEε3 carriers, matched on age, education, and gender. Discussion This study demonstrated a tendency toward BBB permeability in APOEε4 participants emerging from mid‐age, with quantitative differences observable on a number of the measures. While the differences did not reach a statistical significance, the results from this study hint at early changes in ε4 carrier BBB that may help identify at‐risk populations and facilitate the development of early interventions to change the trajectory of decline. This study demonstrated a tendency towards BBB permeability in APOEε4 participants emerging from mid‐age, with quantitative differences observable on a number of the measures. Results from this study may help identify at‐risk populations at an early stage which will be crucial in facilitating the development of early interventions to change the trajectory of decline. To our knowledge, the BBB/APOEε4/healthy mid‐age human studies have not yet been established at the novel age range (45‐59).

Objectives To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. Materials and methods From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. Results The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p  = 0.01 and 0.875vs. 0.804, p  = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p  = 0.006 and 0.890 vs. 0.596, p  = 0.020), but not in predicting histologic grades ( p  = 0.820 and 0.970). Conclusion In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. Clinical relevance statement Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. Key Points • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Objective To evaluate the diagnostic performance of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) for breast cancer identification. Methods A comprehensive electronic systematic searching of Medline, Ovid, EMBASE, Web of Science, CNK, and Cochrane Library databases was performed up to 2 August 2021. Clinical studies associated with DCE‐MRI for breast cancer detection were screened and inlcuded in the meta‐analysis. The data of true positive(tp), false positive(fp), false negative(fn) and true negative(tn) was extracted from includded studies. The sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) were pooled under fixed or random effect models. Publication bias was evaluated by Deek's funnel plot. Results A final set of 15 studies with 1321 breast lesions were included in the present work. The pooled diagnostic sensitivity, specificity, and DOR were 0.87 (95% confidence interval [CI] 0.81–0.92), 0.74 (95% CI 0.68–0.80), and 18.83 (95% CI 9.07–36.54), respectively, and the area under the SROC was 0.86 (95% CI 0.82–0.88). Given a pretest probability of 50%, the positive post‐test probability was 77%, and the negative post‐test probability was 14%. Deek's funnel plot indicated low publication bias (p = 0.61). Conclusion DCE‐MRI is a noninvasive method of breast cancer diagnosis for suspected malignant breast lesions with relative high diagnostic sensitivity and specificity. Likelihood ratio and post‐test probability are important indexes of diagnostic tests. In our meta‐analysis, both the positive and negative likelihood ratio and post‐test probability were moderate. Given a pretest probability of 50%, the positive post‐test probability is 77% and the negative post‐test probability is 14%.