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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.

PURPOSE: Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS: We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS: We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION: Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

Pancreatic acinar cell carcinoma: A comprehensive review.

Acinar cell carcinoma (ACC) is a rare pancreatic malignancy with distinctive clinical, molecular, and morphological features. The long-term survival of ACC patients is substantially superior to that of pancreatic adenocarcinoma patients. As there are no significant patient series about ACCs, our understanding of this illness is mainly based on case reports and limited patient series. Surgical resection is the treatment of choice for patients with the disease restricted to one organ; however, with recent breakthroughs in precision medicine, medicines targeting the one-of-a-kind molecular profile of ACC are on the horizon. There are no standard treatment protocols available for people in which a total surgical resection to cure the condition is not possible. As a result of shared genetic alterations, ACCs are chemosensitive to agents with activity against pancreatic adenocarcinomas and colorectal carcinomas. The role of neoadjuvant or adjuvant chemoradiotherapy has not been established. This article aims to do a comprehensive literature study and present the most recent information on acinar cell cancer.

Apparent diffusion coefficient in differentiation of pediatric posterior fossa tumors.

PURPOSE: To investigate the contribution of preoperative apparent diffusion coefficient (ADC) values in the differential diagnosis of pediatric posterior fossa tumors. METHODS: Forty-two pediatric patients (mean age 7.76 ± 4.58 years) with intra-axial tumors in the infra-tentorial region underwent magnetic resonance imaging. ADC measurement was performed using regions of interest, obtained from the solid component of the mass lesions. ADC ratios were calculated by dividing the ADC values from the mass lesions by the ADC values from normal cerebellar parenchyma. Lesions were categorized as juvenile pilocytic astrocytoma (JPA), ependymoma and medulloblastoma based on histopathological diagnosis. ADC values of the lesions and histopathological diagnoses were statistically correlated. RESULTS: Histopathological diagnosis showed that 14 lesions were JPA, 10 were ependymoma; 18 were medulloblastoma. Both ADC values and ADC ratios were significantly correlated with tumor types (p <0.05). Astrocytoma was distinguished from ependymoma with sensitivity 85.7% and specificity 90% using an ADC ratio ≥1.7 and medulloblastoma was distinguished from ependymoma with sensitivity 100% and specificity 88.89% using an ADC ratio ≤1.18. CONCLUSION: Preoperative ADC values could differentiate the main histological subtypes of pediatric posterior fossa tumors with high sensitivity and specificity.

Susceptibility-Based Differentiation of Intracranial Calcification and Hemorrhage in Pediatric Patients.

Differential diagnosis of intracranial hemorrhage versus calcification on conventional magnetic resonance images (MRIs) is often challenging. Although computed tomography (CT) confirms calcification, phase information obtained during susceptibility-weighted imaging can be useful in distinguishing between 2 pathologies. Fourteen patients previously diagnosed to have hemorrhage or calcification with imaging were included in the study retrospectively. Phase shift values of hemorrhage and calcification were compared by using Student t test. The pathologies identified were tuberous sclerosis, Sturge-Weber syndrome, craniopharyngioma, congenital cytomegalovirus, subependymal hemorrhages, and hemorrhagic microembolic infarction. Calcifications appeared hypointense whereas hemorrhages were hyperintense on phase maps (left-handed magnetic resonance system). Statistical comparison of phase shift values yielded significant difference between hemorrhage versus calcification (P < .01). Phase maps were found to offer valuable data to differentiate 2 pathologies when used complementary to conventional magnetic resonance images. Considering the relatively higher risks of radiation exposure in children, susceptibility-weighted imaging with phase maps may help to waive radiation exposure from CT.