Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

Introduction: Primary central nervous system lymphoma (PCNSL) is rare and accounts for 1-2% of all primary intracranial tumors (ICT). There are conflicting reports regarding the increased incidence of PCNSL over the last two decades in both immunocompromised and immunocompetent patients. Aim: This study was designed to study the clinicopathological characteristics of PCNSL and to access the trend of PCNSL at our institute. Materials and Methods: All the histopathologically proven cases of PCNSL were reviewed from January 1997 to December 2009 (13 years). Immunophenotyping was performed on available paraffin-embedded tissue blocks. Immune status was evaluated and human immunodeficiency virus (HIV) serology was performed in all cases. Cerebrospinal fluid (CSF) findings were recorded whenever available. Possibility of secondary involvement by a systemic lymphoma was excluded in every case. Statistical analysis was done using χ ² -test. Results: During the study period (13 years), a total of 4715 cases of ICT were diagnosed, out of which 66 cases were PCNSL, which accounted for 1.4%. The age ranged from 10 to 75 years with a median age of 46 years. All the patients were immunocompetent. Frontal lobe was the most common site of involvement. Diffuse large B-cell lymphoma was the histological pattern in all the cases. CSF involvement was seen in only one case. Conclusions: In this study, no significant increase in the incidence of PCNSL was found at our institute. Association of PCNSL cases with HIV or acquired immunodeficiency syndrome was not found in our study.

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19–70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p < 0.001) and ADC R3 (p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10⁻⁶ mm² achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making.

BACKGROUND AND PURPOSE: The NI-RADS lexicon doesn9t use ADC parameters and T2 weighted signal for ascribing categories. We explored ADC, DWI, and T2WI to examine the diagnostic accuracy in primary sites of postsurgical oral cavity carcinoma in the Neck Imaging Reporting and Data System (NI-RADS) categories 2 and 3. MATERIALS AND METHODS: We performed a retrospective analysis in clinically asymptomatic post-surgically treated patients with oral cavity squamous cell carcinoma who underwent contrast-enhanced MRI between January 2013 and January 2016. Histopathology and follow-up imaging were used to ascertain the presence or absence of malignancy in subjects with “new enhancing lesions,” which were interpreted according to the NI-RADS lexicon by experienced readers, including NI-RADS 2 and 3 lesions in the primary site. NI-RADS that included T2WI and DWI (referred to as NI-RADS A) and ADC (using the best cutoff from receiver operating characteristic curve analysis, NI-RADS B) was documented in an Excel sheet to up- or downgrade existing classic American College of Radiology NI-RADS and calculate diagnostic accuracy. RESULTS: Sixty-one malignant and 23 benign lesions included in the study were assigned American College of Radiology NI-RADS 2 (n = 33) and NI-RADS 3 (n = 51) categories. The recurrence rate was 90% (46/51) for NI-RADS three, 45% (15/33) for NI-RADS 2, and 73% (61/84) overall. T2WI signal morphology was intermediate in 45 subjects (53.5%) and restricted DWI in 54 (64.2%). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the American College of Radiology NI-RADS were the following: NI-RADS (75.4%, 78.3%, 90.1%, 54.5%, and 76.1%); NI-RADS A (79.1%, 81.2%, 91.9%, 59.1%, and 79.6%); and NI-RADS B (88.9%, 72.7%, 91.4%, 66.7%, and 85.1%), respectively. CONCLUSIONS: Adding MR imaging diagnostic characteristics like T2WI, DWI, and ADC to the American College of Radiology NI-RADS improved diagnostic accuracy and sensitivity.