Metabolic tumor parameters complement clinicopathological factors in prognosticating advanced stage Hodgkin Lymphoma

Objective(s): Advanced Hodgkin Lymphoma has a higher probability of relapse and recurrence. Classical clinicopathological parameters including the International Prognostic Score (IPS) have not been reliable in predicting prognosis or tailoring treatment.  Since FDG PET/CT is the standard of care in staging Hodgkin Lymphoma, this study attempted to evaluate the clinical utility of baseline metabolic tumor parameters in a cohort of advanced Hodgkin lymphoma (stage III and IV).Methods: Histology-proven advanced Hodgkin Patients presenting to our institute between 2012-2016 and treated with chemo-radiotherapy (ABVD / AEVD) were followed up till 2019. Quantitative PET/CT and clinicopathological parameters were used to estimate the Event Free Survival (EFS) in 100 patients. Kaplan-Meier method with log-rank test was used to compare the survival times of prognostic factors.Results: At a median follow-up of 48.83 months (IQR:33.31-63.05 months), the five-year-EFS was 81%. Of the 100 patients, 16 had relapsed (16%) and none died at the last follow-up. On Univariate analysis, among non-PET parameters bulky disease (P=0.03) and B-symptoms (P=0.04) were significant while among PET/CT parameters SUVmax (p=0.001), SUVmean (P=0.002), WBMTV2.5 (P<0.001), WBMTV41% (P<0.001), WBTLG2.5 (P<0.001) and WBTLG41% (P <0.001) predicted poorer EFS.  5-year EFS for patients with low WBMTV2.5 [<1038.3 cm3] was 89% and 35% for patients with high WBMTV2.5 [≥1038.3 cm3] (p <0.001). In a multivariate model, only WBMTV2.5 (P=0.03) independently predicted poorer EFS.Conclusion: PET-based metabolic parameter (WBMTV2.5) was able to prognosticate and complement the classical clinical prognostic factors in advanced Hodgkin Lymphoma. This parameter could have a surrogate value for prognosticating advanced Hodgkin lymphoma. Better prognostication at baseline translates to tailored or risk-modified treatment and hence higher survival

Modeling correlation indices between bladder and Foley's catheter balloon dose with CT-based planning using limited CT slices in intracavitary brachytherapy for carcinoma of cervix

Purpose: To derive and validate an index to correlate the bladder dose with the catheter balloon dose using limited computed tomography (CT) slices. Materials and Methods: Applicator geometry reconstructed from orthogonal radiographs were back-projected on CT images of the same patients for anatomy-based dosimetric evaluation. The correlation indices derived using power function of the catheter balloon dose and the bladder volume dose were validated in 31 patients with cervical cancer. Results: There was significant correlation between International Commission on Radiation Units (ICRU)-38 balloon reference dose (Dr) and the dose received by 25% bladder volume (D 25 ) (P &lt; 0.0001). Significant correlation was also found between the reference dose of mid-balloon point (D rm ) and the dose to D 25 (P &lt; 0.0001). Average percentage difference [100 x (observed index - expected index) / expected index] of observed value of I\u2032 25 (index for the dose to D25 bladder with respect to mid-balloon reference point) from that of expected value was 0.52%, when the index was modeled with reference dose alone. Similarly the average percentage difference for I\u203210cc (index for the dose to 10 cc volume of bladder with respect to mid balloon point) was 0.84%. When this index was modeled with absolute bladder volume and reference dose, standard deviation of the percentage difference between observed and expected index for D rm reduced by approximately 2% when compared to D r . Conclusion: For clinical applications, correlation index modeled with reference dose and volume predicts dose to absolute volume of bladder. Correlation index modeled with reference dose gives a good estimate of dose to relative bladder volume. From our study, we found D rm to be a better indicator of bladder dose than D r

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Impact of immobilisation and image guidance protocol on planning target volume margins for supine craniospinal irradiation

Background: The setup errors during supine-CSI (sCSI) using single or dual immobilisation (SM, DM) subsets from two institutions were reviewed to determine if DM consistently decreased the required planning target volumes (PTV) margins and to identify the optimal image guidance environments. Materials and methods: Ours and a sister institutional cohort, each with a subset of SM or DM sCSI and daily 3-dimensional online image verification sets, were reviewed for the cranial and spinal regions translational shifts. Using descriptive statistics, scatter plots and independent sample Mann-Whitney test we compared shifts in each direction for two subsets in each cohort deriving PTV margins (Van Herk: VH, Strooms: St recipes) for the cranial and spinal regions. Three image guidance (IG) protocols were simulated for two regions on the combined cohort with SM and DM subsets to identify the most optimal option with the smallest PTV margin. The IG protocols: 3F, 5F and 5FB where the systematic error correction was done using the average error from the first three, five and in the cranium alone (applied to both the cranium and spine, otherwise) for the first five set-ups, respectively. Results: 6968 image sets for 179 patients showed DM could consistently reduce the PTV margin (VH/St) for the cranium from 6/5 to 4/3.5 (31.8/30.8%) and 6/4 to 4/3.5 mm (30.5/16.8%) for primary and validation cohort, respectively. Similarly, for the spine it was 10/8.5 to 6/5.5 (38.6/38.4%) and 9/7.7 to 7/6 (21.6/21.4%), respectively. The “5F-IG” resulted in the smallest margins for both the cranial (3 mm) and spinal region (5 mm) for DM with estimated 95% CTV coverage probability. Conclusion: DM with 5F-IG would significantly reduce the required PTV margins for sCSI

Oncolytic immunovirotherapy for high-grade gliomas: A novel and an evolving therapeutic option

Glioblastoma is one of the most difficult tumor types to manage, having high morbidity and mortality with available therapies (surgery, radiotherapy and chemotherapy). Immunotherapeutic agents like Oncolytic Viruses (OVs), Immune Checkpoint Inhibitors (ICIs), Chimeric Antigen Receptor (CAR) T cells and Natural Killer (NK) cell therapies are now being extensively used as experimental therapies in the management of glioblastoma. Oncolytic virotherapy is an emerging form of anti-cancer therapy, employing nature’s own agents to target and destroy glioma cells. Several oncolytic viruses have demonstrated the ability to infect and lyse glioma cells by inducing apoptosis or triggering an anti-tumor immune response. In this mini-review, we discuss the role of OV therapy (OVT) in malignant gliomas with a special focus on ongoing and completed clinical trials and the ensuing challenges and perspectives thereof in subsequent sections