Preoperative CECT-based Radiomic Signature for Predicting the Response of Transarterial Chemoembolization (TACE) Therapy in Hepatocellular Carcinoma.

PURPOSE: To evaluate the efficiency of radiomics signatures in predicting the response of transarterial chemoembolization (TACE) therapy based on preoperative contrast-enhanced computed tomography (CECT). MATERIALS: This study consisted of 111 patients with intermediate-stage hepatocellular carcinoma who underwent CECT at both the arterial phase (AP) and venous phase (VP) before and after TACE. According to mRECIST 1.1, patients were divided into an objective-response group (n = 38) and a non-response group (n = 73). Among them, 79 patients were assigned as the training dataset, and the remaining 32 cases were assigned as the test dataset. METHODS: Radiomics features were extracted from CECT images. Two feature ranking methods and three classifiers were used to find the best single-phase radiomics signatures for both AP and VP on the training set. Meanwhile, multi-phase radiomics signatures were built upon integration of images from two CECT phases by decision-level fusion and feature-level fusion. Finally, multivariable logistic regression was used to develop a nomogram by combining radiomics signatures and clinic-radiologic characteristics. The prediction performance was evaluated by AUC on the test dataset. RESULTS: The multi-phase radiomics signature (AUC = 0.883) performed better in predicting TACE therapy response compared to the best single-phase radiomics signature (AUC = 0.861). The nomogram (AUC = 0.913) showed better performance than any radiomics signatures. CONCLUSION: The radiomics signatures and nomogram were developed and validated for predicting responses to TACE therapy, and the radiomics model may play a positive role in identifying patients who may benefit from TACE therapy in clinical practice.

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins.

PURPOSE: To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. METHODS AND MATERIALS: In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. RESULTS: The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. CONCLUSIONS: The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer.

BACKGROUND: Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis. PURPOSE: To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE. STUDY TYPE: Retrospective. POPULATION: Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set. FIELD STRENGTH/SEQUENCE: A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence. ASSESSMENT: The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets. STATISTICAL TESTS: Fisher's exact test, Mann-Whitney U-test, DeLong test. RESULTS: The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI). DATA CONCLUSION: The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study.

BACKGROUND: Accurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI. METHODS: In total, 5789 annotated LNs (diameter ≥ 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC). FINDINGS: For LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81-0.82. INTERPRETATION: This deep learning-based auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.

A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.

OBJECTIVES: The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lung cancer patients; erroneous evaluations result in inappropriate therapeutic plans and increase the risk of complications. This study aims to develop a cross-modal 3D neural network based on CT images and prior clinical knowledge for accurate prediction of LN metastasis in clinical stage T1 lung adenocarcinoma. PATIENTS AND METHODS: Five hundred one lung adenocarcinoma patients with clinical stage T1 were enrolled. Data including: corresponding 3D nodule-centered patches of CT; prior clinical features; and pathological labels of LN status were obtained. We proposed a cross-modal deep learning system, which can successfully incorporate prior clinical knowledge and CT images into a 3D neural network to predict LN metastasis. We trained and validated our system with 401 cases and tested its performance with 100 cases. The result was compared with that of the logistic regression integration model, the single deep learning model without prior clinical knowledge integration, radiomics method, and manual evaluation by radiologists. RESULTS: The model proposed DensePriNet achieved an AUC of 0.926, which is significantly higher than the logistic regression integration model (0.904) single deep learning model (0.880), and radiomics method (0.891). The Matthews Correlation Coefficient (MCC) of DensePriNet (0.705) was significantly higher than manual classification by one senior radiologist (0.534) and one junior radiologist (0.416), respectively. CONCLUSION: The performance of the single deep learning method is significantly higher than the radiomics method and the radiologists, and integration of prior clinical knowledge into the deep learning model enhance the diagnostic precision of LN status and facilitate the application of precision medicine.

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI.

BACKGROUND: In unresectable hepatocellular carcinoma (HCC), methods to predict patients at increased risk of progression are required. PURPOSE: To investigate the feasibility of radiomics model in predicting early progression of unresectable HCC after transcatheter arterial chemoembolization (TACE) therapy using preoperative multiparametric magnetic resonance imaging (MP-MRI). STUDY TYPE: Retrospective. POPULATION: A total of 84 patients with BCLC B stage HCC from one medical center. According to the modified response evaluation criteria in solid tumors, patients who progressed at 6 months after TACE therapy were assigned as the progressive disease (PD) group (n = 32). Patients whose MRI was performed on four devices were divided into a training cohort (n = 67). Patients whose MRI was performed on other than the previous four devices were used as the testing set (n = 17). FIELD STRENGTH/SEQUENCE: 3.0T, 1.5T axial T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI, b = 0, 500 s/mm2 ), and apparent diffusion coefficient (ADC) ASSESSMENT: PD was confirmed via imaging studies with MRI. Risk factors, including age, alpha fetoprotein (AFP), size, and radiomic-related features of PD were assessed. In addition, the discrimination ability of each radiomics signature was tested on an independent testing set. STATISTICAL TESTS: The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and testing sets. The results indicated that the MP-MRI model achieved the greatest benefit. RESULTS: In the testing set, the model based on DWI features presented an AUC of (b = 0, 0.786; b = 500, 0.729), followed by T2 WI features (0.729) and ADC (0.714). The AUC of the MP-MRI signature was increased to 0.800 compared to any single MRI signature. The multivariate logistic analysis identified the radiomics signature as independent parameters of PD, while clinical information such as age, AFP, size, etc., had no significance in the PD group. DATA CONCLUSION: Preoperative MP-MRI has the potential to predict the outcome of TACE therapy for unresectable HCC. In addition, these image features may be complementary to the current staging systems of HCC patients. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3. J. Magn. Reson. Imaging 2020;52:1083-1090.