Multiparametric Magnetic Resonance Imaging Images Research Articles

The Diagnostic Value of Multiparametric-MRI in Locating Prostate Cancer in Comparison with Transrectal Ultrasound-Guided Biopsy

Purpose: The present study was designed to evaluate the potential efficacy of Multiparametric-Magnetic Resonance Imaging (MP-MRI) in the detection of prostate cancer locations compared to Transrectal Ultrasound (TRUS) guided biopsy, as the gold standard method.
 Materials and Methods: A total of 66 subjects participated in this cross-sectional study. All individuals underwent MP-MRI imaging before the prostate TRUS. The findings of either method have been investigated and the comparison had been made using the Chi-squared test.
 Results: The sensitivity and specificity of the MP-MRI in the diagnosis of prostate cancer were 81.8% and 93.9%, respectively. The positive and negative predictive values were 93.1% and 83.8%, respectively.
 Conclusion: The current study indicates that the MP-MRI imaging method has sufficient sensitivity and specificity for detecting the location of prostate cancer and can potentially be employed as a clue-providing method prior to the TRUS-guided biopsy.

MRI-based radiomic features for identifying recurrent prostate cancer after proton radiation therapy.

Magnetic Resonance Imaging (MRI) evaluation of recurrent prostate cancer (PCa) following proton beam therapy is challenging due to radiation-induced tissue changes. This study aimed to evaluate MRI-based radiomic features so as to identify the recurrent PCa after proton therapy. We retrospectively studied 12 patients with biochemical recurrence (BCR) following proton therapy. Two experienced radiologists identified prostate lesions from multi-parametric MRI (mpMRI) images post-proton therapy and marked control regions of interest (ROIs) on the contralateral side of the prostate gland. A total of 210 radiomic features were extracted from lesions and control regions on the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) image series. Recursive Feature Elimination with Cross-Validation method (RFE-CV) was used for feature selection. A Multilayer Perceptron (MLP) neural network was developed to classify three classes: cancerous, benign, and healthy tissue. The 12-core biopsy results were used as the gold standard for the segmentations. The classifier performance was measured using specificity, sensitivity, the area under receiver operating characteristic curve (AUC), and other statistical indicators. Based on biopsy results, 10 lesions were identified as PCa recurrence while eight lesions were confirmed to be benign. Ten radiomic features (10/210) were selected to build the multi-class classifier. The radiomics classifier gave an accuracy of 0.83 in identifying cancerous, benign, and healthy tissue with a sensitivity of 0.80 and specificity of 0.85. The model yielded an AUC of 0.87, 95% CI [0.72-1.00] in differentiating cancer from the benign and healthy tissues. Our proof-of-concept study demonstrates the potential of using radiomic features as part of the differential diagnosis of PCa on mpMRI following proton therapy. The results need to be validated in a larger cohort.

Targeted augmented reality-guided transperineal prostate biopsies study: initial experience.

Transperineal biopsy of magnetic resonance imaging (MRI)-detected prostate lesions is now the established technique used in prostate cancer (CaP) diagnostics. Virtual Surgery Intelligence (VSI) Holomedicine by Apoqlar (Hamburg, Germany) is a mixed reality (MR)/augmented reality (AR) software platform that runs on the HoloLens II system (Microsoft, Redford, USA). Multiparametric prostate MRI images were converted into 3D holograms and added into a MR space, enabling visualization of a 3D hologram and image-assisted prostate biopsy. The Targeted Augmented Reality-GuidEd Transperineal (TARGET) study investigated the feasibility of performing AR-guided prostate biopsies in a MR framework, using the VSI platform in patients with MRI-detected prostate lesions. Ten patients with a clinical suspicion of CaP on MRI (Prostate Imaging-Reporting and Data System, PI-RADS 4/5) were uploaded to the VSI HoloLens system. Two MR/AR-guided prostate biopsies were then acquired using the PrecisionPoint Freehand transperineal biopsy system. Cognitive fusion biopsies were performed as standard of care following the MR/AR-guided prostate biopsies. All 10 patients successfully underwent MR/AR-guided prostate biopsy after 3D MR images were overlaid on the patient's body. Prostatic tissue was obtained in all MR/AR-guided specimens. Seven patients (70%) had matching histology in both the standard and MR/AR-guided biopsies. The remaining three had ISUP (International Society of Urological Pathology) Grade 2 CaP. There were no immediate complications. We believe this is a world first. The initial feasibility data from the TARGET study demonstrated that an MR/AR-guided prostate biopsy utilizing the VSI Holomedicine system is a viable option in CaP diagnostics. The next stage in development is to combine AR images with real-time needle insertion and to provide further data to formally appraise the sensitivity and specificity of the technique.

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Vasari-Based Features Nomogram to Predict the Tumor-Infiltrating CD8+ T Cell Levels in Glioblastoma

Tumor-infiltrating CD8+T cells play a key role inglioblastoma (GB) development, malignant progression, and recurrence. The aim of the study was to establish nomograms based on the Visually AcceSAble Rembrandt Images (VASARI) features of multiparametric magnetic resonance imaging (MRI) to determine the expression levels of tumor-infiltrating CD8+T cells in patients with GB. Pathological and imaging data of 140 patients with GB confirmed by surgery and pathology were retrospectively analyzed. The levels of tumor-infiltrating CD8+T cells in tumor tissue samples obtained from patients were quantified using immunohistochemical staining. Patients were divided into high and low CD8 expression groups. The MRI images of patients with GBwere analyzed by two radiologists using the VASARI scoring system. A total of 25 MRI-based VASARI imaging features were evaluated by two neuroradiologists. The features with the greatest predictive power for CD8 expression levels were, cystic (OR, 3.063; 95% CI: 1.387, 6.766; P=0.006), hemorrhage (OR, 2.980; 95% CI: 1.172, 7.575; P=0.022), and ependymal extension (OR, 0.257; 95% CI: 0.114 0.581; P=0.001). A logistic regression model based on these three features showed better sample predictive performance (AUC=0.745; 95% CI: 0.665, 0.825; Sensitivity=0.527; Specificity=0.857). The VASARI feature-based nomogram model can showpromise to predict the level of infiltrative CD8 expression in GB tumors non-invasively for earlier tissue diagnosis and more aggressive treatment.

Physical Activity Attenuates Brain Irradiation-Associated Skeletal Muscle Damage in the Rat

Background: Radiotherapy for brain tumors increases patient survival. Nonetheless, side effects are increasingly reported such as cognitive deficits and fatigue. The etiology of fatigue remains poorly described. Our hypothesis is that the abscopal effects of radiotherapy on skeletal muscle may be involved in fatigue. The present study aims to assess the impact of brain irradiation on skeletal muscles, its relationships with fatigue and to analyze whether physical activity could counteract brain radiation-induced side effects. Methods: Adult Wistar rats were randomly distributed between 4 groups: control (CTL), irradiated (IR), non-irradiated with physical activity (PA) and irradiated with physical activity (IR+PA). Irradiated rats were exposed to a whole-brain irradiation (WBI) of 30 Gy (3×10 Gy). Rats subjected to PA underwent sessions of running on a treadmill, 3 times/week during 6 months. The effects of WBI on muscles were evaluated by complementary approaches: behavioral tests (fatigue, locomotion activity), magnetic resonance imaging (MRI) and histological analyses. Results: Irradiated rats displayed a significant fatigue and a reduced locomotor activity at short term compared to the CTL group which were attenuated with PA at 6 months after whole-brain irradiation. The IR rat's gastrocnemius mass decreased compared to CTL rats which was reversed by physical activity at 14 days after WBI. Multiparametric MRI imaging of the skeletal muscle highlighted an alteration of the fiber organization in IR rats as demonstrated by a significant decrease of the mean diffusivity in the gastrocnemius at short term. Alteration of fibers was confirmed by histological analyses: the number of type I fibers was decreased while that of type IIa fibers was increased in irradiated animals but not in IR+PA group. Conclusions: The data show that whole-brain irradiation induces skeletal muscle damage which are attenuated by PA. This muscle damage may explain, at least in part, the fatigue of patients treated with radiotherapy.

The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM).

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.

Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.

To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model

Cervical malignant growth is the fourth most typical reason for disease demise in women around the world. In developing countries, women don’t approach sufficient screening methods because of the costly procedures to undergo examination regularly, scarce awareness and lack of access to the medical centre. Recently, deep learning-based radiomic methods have been introduced in differentiating vessel invasion from non-vessel invasion in Cervical Cancer (CC) by multi-parametric Magnetic Resonance Imaging (MRI). However, this model doesn’t produce sufficient results. In this work, the MRI images are initially pre-processed using bilateral filtering. After pre-processing, the image is segmented by modified U-Net model in order to identify the cancerous region. Extraction of deep semantic information from images by using residual blocks in the processes of contractions and expansions. The last layer of the contracting route uses tightly coupled convolutions in the second phase to speed up feature recycling and feature propagation. It was inferred from the observations that the proposed model was effective as a predictive tool for detecting vessel invasions in preoperative early stages of CC. Proposed model produces 94.00% detection accuracy which is better than the other existing methods.

Промежностная фьюжн-биопсия предстательной железы. Диагностическая ценность в сравнении со стандартной биопсией.

Introduction. A significant improvement in the quality of prostate imaging by magnetic resonance (MRI), as well as the possibility of combining MRI and ultrasound images (fusion technology) for biopsy, open up new possibilities for the diagnosis of prostate cancer. The arsenal of methods of treating prostate cancer includes active surveillance and focal therapy for patients with low risk disease. That raises the question of improving the accuracy of diagnosis of tumor volume, stage and differentiation, which will largely determine the tactics of patient management. The aim of our work was to determine the diagnostic effectiveness of targeted MRI-assisted fusion biopsy of the prostate in comparison with standard biopsy. Materials and methods. We analyzed data of 356 patients who underwent tranperineal fusion biopsy. The age of the patients ranged from 48 to 72 years with an average value of 68.2 ± 2.1 years. The selection criteria for the study were an increase in the level of PSA (345 patients) – the range from 4 to 44.5 ng/ml (on average 6.02±0.21) or the detection of tumor-suspicious areas during finger rectal examination or transrectal ultrasound examination (TRUS) of the prostate gland (41 patients). Further methods of examination included multiparametric magnetic resonance imaging (MRI) and subsequent transperineal fusion biopsy in combination with standart biopsy. Results. In our study, the detection rate of prostate cancer was (82.6%) – the diagnosis was confirmed in 294 out of 356 examined patients. At the same time, with an increase in the PI-RADS gradation, the frequency of histological confirmation of cancer increased: 62.4% for PI-RADS 3 lesions, 89.6% for 4 and 96.7% for PI-RADS 5. Targeted biopsies confirmed the presence of prostate cancer in 246 cases out of 356 patients (69.1%). In relation to all cases of cancer detection, the proportion of positive targeted biopsies was 83.7%. Conclusion. Targeted MRI-assisted fusion biopsy allows detecting prostate cancer in 69.1%, but when supplemented with a polyfocal biopsy at standard points, the diagnostic accuracy increases to 82.6%. Thus, the data obtained allow us to conclude that based on the examination of patients with suspected prostate cancer using mpMRI with targeted fusion biopsy, supplemented by polyfocal biopsies at standard points, it is possible to determine the localization, extent and degree of malignancy of the tumor with a high degree of certainty.

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling.

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI

PurposeIdentifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). Patients and methodsThis retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. ResultsWith all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. ConclusionThe proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

MRI correlates of motoneuron loss in SMA

BackgroundMagnetic resonance imaging (MRI) is currently explored as supplemental tool to monitor disease progression and treatment response in various neuromuscular disorders. We here assessed the utility of a multi-parametric magnetic resonance imaging (MRI) protocol including quantitative water T2 mapping, Dixon-based proton density fat fraction (PDFF) estimation and diffusion tensor imaging (DTI) to detect loss of spinal motor neurons and subsequent muscle damage in adult SMA patients.MethodsSixteen SMA patients and 13 age-matched controls were enrolled in this prospective, longitudinal study. All participants underwent MRI imaging including measurements of Dixon-based PDFF and DTI of the sciatic nerve. SMA patients furthermore underwent measurements of muscle water T2 (T2w) of the biceps femoris muscle (BFM) and quadriceps femoris muscle (QFM). Ten participants returned for a second scan six months later. MRI parameter were correlated with clinical data. All patients were on nusinersen treatment.ResultsThere were significantly higher intramuscular fat fractions in the BFM and QFM of SMA patients compared to healthy controls at baseline and after 6 months. Furthermore, T2 values significantly correlated positively with intramuscular fat fractions. The Hammersmith functional motor scale significantly correlated with the QFM’s intramuscular fat fractions. DTI scans of the sciatic nerve were not significantly different between the two groups.ConclusionThis study demonstrates that, water T2 mapping and Dixon-based PDFF estimation may distinguish between adult SMA patients and controls, due to massive intramuscular fat accumulation in SMA. More extensive long-term studies are warranted to further evaluate these two modalities as surrogate markers in SMA patients during treatment.

Clinical-Radiomics Nomogram from T1W, T1CE, and T2FS MRI for Improving Diagnosis of Soft-Tissue Sarcoma.

To compare values of multiparametric magnetic resonance imaging (MRI) sequences and propose clinical-radiomics nomogram for diagnosis of soft-tissue sarcoma (STS). This study enrolled 148 patients from Dec. 2017 to Feb. 2021. All patients underwent T1-weighted (T1W), contrast-enhanced T1-weighted (T1CE), and T2-weighted fat-suppressed (T2FS) MRI scans. A total of 1967 radiomic features were extracted from the segmented regions of interest (ROIs) in each MRI sequence. Highly diagnostic radiomic features were selected with Mann-Whitney U test, elastic net, and Akaike's information criterion (AIC) based on MRI images. Logistical regression was used to build Rad scores. Clinical factors were analyzed using the chi-square test or Mann-Whitney U test. The performance of the Rad scores was judged using the area under the receiver operating characteristic area under the curve (ROC AUC), sensitivity, specificity, and accuracy. The nomogram was developed by integrating the Rad score and the most important clinical factor. By combining the three MRI sequences, the Rad-Com was developed consisting of twelve features selected by with Mann-Whitney U test, elastic net, and AIC: four from T1W, three from TICE, and five from T2FS MRI. The margin (P < 0.05) demonstrated a statistically significant difference between patients with benign and malignant soft-tissue tumors (STT). The nomogram was constructed by integrating the Rad-Com and margin, which yielded favorable diagnostic AUCs of 0.919 (sensitivity (Sen) = 0.784, specificity (Spe) = 0.936) and 0.913 (Sen = 0.923, Spe = 0.792) in the training and validation cohort. The proposed nomogram may have potential as a noninvasive marker for STS diagnosis.

A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

PurposeThis study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC).MethodsThis study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers.ResultsFor each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts.ConclusionsThe tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

MRI Radiomics in Prostate Cancer: A Reliability Study.

BackgroundRadiomics can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols, as well as by the segmentation of the region of interest (ROI). Prostate cancer (PCa) represents a difficult playground for this technique, due to discrepancies in the identification of the cancer lesion and the heterogeneity of the acquisition protocols. The aim of this study was to investigate the reliability of radiomics in PCa magnetic resonance imaging (MRI).MethodsA homogeneous cohort of patients with a PSA rise that underwent multiparametric MRI imaging of the prostate before biopsy was tested in this study. All the patients were acquired with the same MRI scanner, with a standardized protocol. The identification and the contouring of the region of interest (ROI) of an MRI suspicious cancer lesion were done by two radiologists with great experience in prostate cancer (>10 years). After the segmentation, the texture features were extracted with LIFEx. Texture features were then tested with intraclass coefficient correlation (ICC) analysis to analyze the reliability of the segmentation.ResultsForty-four consecutive patients were included in the present analysis. In 26 patients (59.1%), the prostate biopsy confirmed the presence of prostate cancer, which was scored as Gleason 6 in 6 patients (13.6%), Gleason 3 + 4 in 8 patients (18.2%), and Gleason 4 + 3 in 12 patients (27.3%). The reliability analysis conversely showed poor reliability in the majority of the MRI acquisition (61% in T2, 89% in DWI50, 44% in DWI400, and 83% in DWI1,500), with ADC acquisition only showing better reliability (poor reliability in only 33% of the texture features).ConclusionsThe low ratio of reliability in a monoinstitutional homogeneous cohort represents a significant alarm bell for the application of MRI radiomics in the field of prostate cancer. More work is needed in a clinical setting to further study the potential of MRI radiomics in prostate cancer.

Transperineal multiparametric magnetic resonance imaging-ultrasound fusion–targeted prostate biopsy combined with standard template improves perineural invasion detection

Perineural invasion (PNI) on biopsy is associated with adverse features in prostate cancer (PCa). Transrectal multiparametric magnetic resonance imaging (MRI)-targeted biopsy (TBx) has shown to detect higher presence of PNI than standard template biopsy (SBx). Transperineal biopsy provides effective cancer detection with lower complications than the transrectal approach. We compared PNI detection efficiency between SBx and TBx through transperineal approach. We identified patients with PCa who underwent transperineal TBx and concomitant standard 20-core template SBx from September 2019 to February 2021. Clinical, MRI imaging and biopsy characteristics were evaluated and compared between TBx and SBx. Two hundred thirty-eight patients with PCa underwent concomitant transperineal SBx and TBx procedures. Combined PNI+ (SBxPNI+ and/or TBxPNI+) was identified in 77 of 238 (32.4%) patients. SBx detected 23.9% PNI-positive patients and TBx detected 19.3% PNI-positive patients of all patients with PCa. Patients with PNI were with significantly different clinicopathological characteristics than patients without PNI. Although significantly more positive PCa cores and higher positive PCa core rate were found in the SBx method, patients with SBxPNI+ only shared similar features as TBxPNI+only patients. Of 176 cases with both SBxPCa and TBxPCa, TBx could detect 19 (15.1%) more PNI cases than SBx while SBx could detect 24 (18.3%) more PNI cases than TBx. Multiparametric MRI fusion-targeted biopsy in combination with template biopsy through transperineal approach achieved PNI detection rate over 30% of PCa cases. The increased PNI detection may improve the model to select active surveillance candidates in clinical practice.

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET

BackgroundThe purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsupervised, two-level clustering approach followed by support vector machine in order to classify the isocitrate dehydrogenase (IDH) status of gliomas.MethodsSixty-two treatment-naïve glioma patients who underwent FDOPA PET and MRI were retrospectively included. Contrast enhanced T1-weighted images, T2-weighted images, fluid-attenuated inversion recovery images, apparent diffusion coefficient maps, and relative cerebral blood volume maps, and FDOPA PET images were used for voxel-wise feature extraction. An unsupervised two-level clustering approach, including a self-organizing map followed by the K-means algorithm was used, and each class label was applied to the original images. The logarithmic ratio of labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. The area under the curve (AUC) of receiver operating characteristic curves, accuracy, and F1-socore were calculated and used as metrics for performance.ResultsThe associations of multiparametric imaging values in each cluster were successfully visualized. Multiparametric images with 16-class clustering revealed the highest classification performance to differentiate IDH status with the AUC, accuracy, and F1-score of 0.81, 0.76, and 0.76, respectively.ConclusionsMachine learning using an unsupervised two-level clustering approach followed by a support vector machine classified the IDH mutation status of gliomas, and visualized voxel-wise features from multiparametric MRI and FDOPA PET images. Unsupervised clustered features may improve the understanding of prioritizing multiparametric imaging for classifying IDH status.