External validation of the Brain Tumour Reporting and Data System (BT-RADS) in the multidisciplinary managementof post-treatment gliomas.

Purpose: To independently and externally validate the Brain Tumour Reporting and Data System (BT-RADS) for post-treatment gliomas and assess interobserver variability. Material and methods: In this retrospective observational study, consecutive MRIs of 100 post-treatment glioma patients were reviewed by two independent radiologists (RD1 and RD2) and assigned a BT-RADS score. Inter-observer agreement statistics were determined by kappa statistics. The BT-RADS-linked management recommendations per score were compared with the multidisciplinary meeting (MDM) decisions. Results: The overall agreement rate between RD1 and RD2 was 62.7% (κ = 0.67). The agreement rate between RD1 and consensus was 83.3% (κ = 0.85), while the agreement between RD2 and consensus was 69.3% (κ = 0.79). Among the radiologists, agreement was highest for score 2 and lowest for score 3b. There was a 97.9% agreement between BT-RADS-linked management recommendations and MDM decisions. Conclusions: BT-RADS scoring led to improved consistency, and standardised language in the structured MRI reporting of post-treatment brain tumours. It demonstrated good overall agreement among the reporting radiologists at both extremes; however, variation rates increased in the middle part of the spectrum. The interpretation categories linked to management decisions showed a near-perfect match with MDM decisions.

Oestrogen Receptor Positive Metastatic Eccrine Porocarcinoma: A Case Report and Review of Anti-Oestrogen Therapy in Rare Cancers.

Introduction: Rare cancers, in aggregate, represent a significant burden of disease in oncology and remain therapeutically challenging to manage due to a lack of clinical trials. Eccrine porocarcinoma is a rare cutaneous sweat-gland malignancy for which there remains no standard approach to metastatic disease. Case Presentation: We describe a patient diagnosed with metastatic disease, confirmed on bone biopsy; pathological analysis further revealed this was oestrogen receptor positive. She was commenced on the aromatase inhibitor letrozole, and denosumab, and showed a significant clinical and radiological response on bone scan within 7 months. At the time of report, over 2 years since commencing letrozole, she remains well with no evidence of progression. Conclusion: Our experience adds to the literature suggesting anti-oestrogen therapy can have significant benefit in patients with ER-positive non-breast cancer and is in keeping with increasing interest in therapies agnostic to site of origin but guided by expression/mutation of oncogenic drivers.

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging.

PURPOSE: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. METHODS: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. RESULTS: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model's accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). CONCLUSIONS: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction.

A case of immunotherapy-induced thyroiditis.

Immunotherapy treatments for cancer are known to cause adverse thyroid events which present a diagnostic challenge to clinicians and radiologists. This case report highlights the importance of a high clinical index of suspicion and careful assessment of the thyroid on serial imaging studies to make the diagnosis. The case involves a 65-year-old male with malignant melanoma who was started on immunotherapy as part of a clinical trial. He developed thyroid dysfunction followed by an attack of acute neck pain. Ultrasound of his thyroid was performed which showed significant atrophy. A review of previous imaging was undertaken which confirmed the patient had suffered from thyroiditis and subsequent atrophy. Following this, the diagnosis of immunotherapy-induced thyroid dysfunction was made. Thyroxine supplementation and steroid dose were then adjusted causing his thyroid function and symptoms to improve. Immunotherapy agents for cancers are becoming more and more common. As the case report shows, physicians and radiologists will need to be vigilant to diagnose and treat any adverse events.

A Comparative Study Evaluating the Quality of Life and Survival Outcomes in Patients Receiving Chemotherapy Versus Oral Tyrosine Kinase Inhibitor in the Third Line and Beyond Setting for Advanced NSCLC.

Introduction: The outcomes in advanced NSCLC have improved owing to the availability of more effective systemic and improved supportive care. This has increased the number of patients who seek treatment in the third line and beyond setting. We conducted this study to compare the quality of life (QoL), toxicity, and outcomes in patients receiving chemotherapy and EGFR tyrosine kinase inhibitors (TKIs) in this setting. Methods: In this phase 3, randomized, open-label study, patients with stage III or IV NSCLC with disease progression on at least two prior lines of chemotherapy, with a life expectancy of at least 3 months, without prior EGFR TKI exposure, and stable brain metastases (if any) were included. Patients were randomized to receive chemotherapy (gemcitabine or docetaxel or paclitaxel or vinorelbine) or an EGFR TKI (erlotinib or gefitinib). The primary end point was the change in QoL at 8 to 10 weeks; the secondary outcomes were safety and overall survival (OS). Patients underwent clinical evaluation at every visit, and toxicity was assessed as per Common Terminology Criteria for Adverse Events version 4.03. A radiological tumor response assessment was done every 8 to 12 weeks from the start of therapy. The QoL was assessed using the EORTC QLQ C30 and LC13 questionnaires. The change in QoL scores was calculated as the difference between scores at baseline and scores at 8 to 10 weeks (Δ) for each QoL domain. The Mann-Whitney U test was used to compare the mean difference (Δ) for each domain. OS and progression-free survival (PFS) were determined using the Kaplan-Meier method and Cox proportional regression analysis. Results: A total of 246 patients were enrolled in the study, with 123 in each arm. There was a male predominance with 69.1% male patients in the chemotherapy arm and 70.7% in the EGFR TKI arm. The median age of patients in the chemotherapy arm was 54 years and 55 years in the chemotherapy and EGFR TKI arms, respectively. There was no significant difference in the change in QoL at baseline and the second visit (Δ) in both arms in all domains of EORTC QLQ C30 except cognitive function (p = 0.0045) and LC13 except alopecia (0.01249). The mean Δ Global Health Status was -28 in the chemotherapy arm and -26.8 in the EGFR TKI arm; this was not statistically significant (p = 0.973). The median follow-up was 88.1 months (95% confidence interval [CI]: 39.04-137.15). On the intention-to-treat analysis, the median PFS was 3.13 months (95% CI: 2.15-4.11) in the chemotherapy arm and 2.26 months (95% CI: 2.1-2.43) in the EGFR TKI arm, with hazard ratio at 1.074 (95% CI: 0.83-1.38) (p = 0.58). There were 120 deaths in each arm. The median OS was 7.63 months (95% CI: 5.96-9.30) in the chemotherapy arm and 7.5 months in the EGFR TKI arm (95% CI: 5.85-9.14); hazard ratio at 1.033 (95% CI: 0.80-1.33) (p = 0.805). The toxicity profile was similar in both arms except for a significantly higher incidence of fatigue (p = 0.043), peripheral neuropathy (0.000), alopecia, hypokalemia (0.037), and pedal edema (0.007) in the chemotherapy arm and dry skin (p = 0.000) and skin rash (p = 0.019) in the EGFR TKI arm. Conclusions: There was no significant difference in most QoL scales (except cognitive function and alopecia), OS, and PFS of patients with advanced NSCLC receiving an EGFR TKI as compared with chemotherapy TKI in the third-line setting. The toxicity profile is consistent with the known toxicities of the agents.

Role of imaging in the management of thyroglossal duct cyst carcinomas (TGC-TIRADS): a single centre retrospective study over 16 years.

Introduction: Thyroglossal duct cyst (TGDC) is the most frequently encountered developmental anomaly in thyroid genesis with a reported incidence of 7% in the adult population. The cyst is known to develop anywhere along the pathway of thyroid descent but is more frequently seen in the infrahyoid neck in the midline. The incidence of malignancy in a TGDC is approximately 1%; a majority of these are papillary carcinomas. This study was conducted at a single tertiary care centre which spanned over a decade which adds practice changing evidence-based knowledge to existing literature on this rare entity. A comprehensive study which conclusively establishes the imaging features predictive of malignancy in TGDC carcinomas (TGDCa), the protocol for optimal management, clinical outcome and long-term survival of these patients is not available. Although TGDC carcinoma is thought to have an excellent prognosis, there is not enough data available on the long-term survival of these patients. The aim of this study was to identify whether neck ultrasound (US) can serve as an accurate imaging tool for the preoperative diagnosis of TGDC carcinomas. Methods: We accessed the electronic medical records of 86 patients with TGDC between January 2005 to December 2021. Of these, 22 patients were detected with TGDC papillary carcinoma on histopathologic examination. Relevant imaging, treatment and follow up information for all cases of TGDC carcinoma were retrospectively reviewed. We compared US characteristics predictive of malignancy across outcomes groups; malignant vs benign using the Chi-square test. Based on the results, a TGC-TIRADS classification was proposed with calculation of the percentage likelihood of malignancy for each category. Results: Compared to benign TGDCs, malignant TGDCs were more likely to present with following US characteristics: irregular or lobulated margins (90.40 vs. 38.10%), solid-cystic composition (61.90 vs. 17.07%), internal vascularity (47.62 vs. 4.88 %), internal calcification (76.19 vs. 7.32 %) (each p value < 0.005). Calcifications and internal vascularity were the most specific while irregular/lobulated margins were the most sensitive feature for malignancy. AUC under the ROC curve was 0.88. Allpatients were operated and were disease free at the end of 5 years or till the recent follow up. Discussion: US is the imaging modality of choice for pre-operative diagnosis of TGDC carcinoma. Thepre-operative diagnosis and risk stratification of thyroglossal lesions will be aided by the application of the proposed TGC-TIRADS classification, for which the percentage likelihood of malignancy correlated well with the results in our study. Sistrunk procedure is adequate for isolated TGDC carcinoma; suspicious neck nodes on imaging also necessitates selective nodal dissection. Papillary carcinomas have an excellent prognosis with low incidence of disease recurrence.

Repurposing pantoprazole in combination with systemic therapy in advanced head and neck squamous cell carcinoma: a phase I/II randomized study.

Pantoprazole decreases the acidity of the tumor microenvironment by inhibiting proton pumps on the cancer cell. This possibly leads to increased sensitivity to cytotoxic therapy. We conducted a phase I/II randomized controlled trial in adult patients with head and neck squamous cell carcinoma (HNSCC) planned for first-line palliative chemotherapy. Patients were randomized to chemotherapy + / - intravenous (IV) pantoprazole. The primary endpoint in phase I was to determine the maximum safe dose of intravenous pantoprazole, whereas it was progression-free survival (PFS) in phase II. The dose of IV pantoprazole established in phase I was 240 mg. Between Nov'18 and Oct'20, we recruited 120 patients in phase II, 59 on pantoprazole and 61 on the standard arm. Median age was 51 years (IQR 43-60), 80% were men. Systemic therapy was IV cisplatin in 22% and oral-metronomic-chemotherapy (OMC) in 78%. Addition of pantoprazole did not prolong PFS, which was 2.2 months (95% CI 2.07-3.19) in the pantoprazole arm and 2.5 months (95% CI 2.04-3.81, HR, 1.14; 95% CI 0.78-1.66; P = 0.48) in the standard arm. Response rates were similar; pantoprazole arm 8.5%, standard arm 6.6%; P = 0.175. Overall survival was also similar; 5.6 months (95% CI 4.47-8.51) in the pantoprazole arm and 5.4 months (95% CI 3.48-8.54, HR 1.06; 95% CI 0.72-1.57; P = 0.75) in the standard arm. Grade ≥ 3 toxicities were similar. Thus, pantoprazole 240 mg IV added to systemic therapy does not improve outcomes in patients with advanced HNSCC.

Role of computed tomography in the evaluation of regional metastasis in well-differentiated thyroid cancer.

Background: Accurate neck staging is essential for performing appropriate surgery and avoiding undue morbidity in thyroid cancer. The modality of choice for evaluation is ultrasonography (US), which has limitations, particularly in the central compartment, that can be overcome by adding a computed tomography (CT). Methods: A total of 314 nodal levels were analyzed in 43 patients with CT, and US; evaluations were done between January 2013 and November 2015. The images were reviewed by two radiologists independently who were blinded to histopathological outcomes. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy of US, CT, and US + CT were calculated using histology as the gold standard. Results: The overall sensitivity, specificity, PPV, and NPV for US, CT, and US + CT were 53.9%, 88.8%, 74.1%, and 76.4%; 81.2%, 68.0%, 60.1%, and 85.9%; and 84.6%, 66.0%, 59.6%, and 87.8%, respectively. The overall accuracy of the US was 75.80%, the CT scan was 72.93%, and the US + CT scan was 72.93%. For the lateral compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 56.6%, 91.4%, 77.1%, and 80.5%; 80.7%, 70.6%, 58.3%, and 87.8%; and 84.3%, 68.7%, 57.9%, and 89.6%, respectively. The accuracy of the US was 79.67%, the CT scan was 73.98%, and the US + CT scan was 73.98% for the lateral compartment. For the central compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 47.1%, 76.5%, 66.7%, and 59.1%; 82.4%, 55.9%, 65.1%, and 76.0%; and 85.3%, 52.9%, 64.4%, and 78.3%, respectively. The accuracy of the US was 61.76%, the CT scan was 69.12%, and the US + CT scan was 69.12% for the central compartment. Conclusions: This study demonstrated that CT has higher sensitivity in detecting nodal metastasis; however, its role is complementary to US due to low specificity.

Long-Term Outcomes of Crizotinib Treated ALK-Positive Lung Cancer Patients: A Retrospective Audit of Prospective Data from Resource-Constrained Settings.

Akhil KapoorPurpose Crizotinib has been one of the standard treatment options for the treatment of anaplastic lymphoma kinase (ALK) rearranged non-small cell lung cancer (NSCLC) based on higher progression-free survival (PFS) and objective response rates in phase III clinical trials. However, real-world data about the long-term efficacy and toxicity of crizotinib is limited. Methods A retrospective analysis of all patients with ALK-positive NSCLC, treated with crizotinib between March 2014 and December 2016, was performed. The main outcomes measured were PFS, overall survival (OS), and adverse effects. Results One hundred and eighty-eight patients treated with crizotinib during this period were included in this study. The median age was 50 years (range: 24-74) with a majority being males (73.2%) and 80.3% with a performance status of 0 to 1. The median duration of follow-up was 49.4 months (range: 3.4-86.3%). The median PFS of crizotinib was 17.3 months (95% confidence interval [CI], 13.0-21.6) and 12.8 months (95% CI, 8.1-17.6) when used in first line or subsequent lines, respectively. The median OS was 38.3 months (95% CI, 28.4-48.2). The patients who received crizotinib in the first line had a median OS of 45.5 months (95% CI, 29.6-61.4) as compared with 29.7 months (95% CI, 22.2-37.2) for those who received in subsequent line (hazard ratio, 0.6, 95% CI, 0.4-0.9, p =0.022). The most common all grade toxicities include transaminitis, anemia, fatigue, and corrected QT prolongation. Conclusion This real-world study confirms the long-term beneficial effects of crizotinib in ALK rearranged NSCLC with favorable toxicity profile like that of the registration studies, in resource constrained settings.

Exploring the implications of modified advanced lung cancer inflammation index on outcomes in patients with advanced non-small cell lung cancer.

Aim: Sarcopenia and skeletal muscle density (SMD) have been shown to be both predictive and prognostic marker in oncology. Advanced lung cancer inflammation index (ALI) has been shown to predict overall survival (OS) in small cell lung cancer (SCLC). Computed tomography (CT) enables skeletal muscle to be quantified, whereas body mass index (BMI) cannot accurately reflect body composition. The purpose was to evaluate the prognostic value of modified ALI (mALI) using CT-determined third lumbar vertebra (L3) muscle index beyond original ALI and see the interaction between sarcopenia, SMD, neutrophil-lymphocyte ratio (NLR), ALI and mALI at baseline and post 4 cycles of chemotherapy and their effects on OS and progress free survival (PFS) in patients with advanced non-SCLC (NSCLC). Methods: This retrospective study consisted of a total of 285 advanced NSCLC patients. The morphometric parameters such as SMD, skeletal muscle index (SMI) and fat-free mass (FFM) were measured by CT at the L3 vertebra. ALI was defined as BMI × serum albumin/NLR and mALI was defined as SMI × serum albumin/NLR. Results: Sarcopenia was observed in over 70% of patients across all BMI categories. Patients having sarcopenia suffered from a higher incidence of chemotherapeutic drug toxicities but this was not found to be statistically significant. Concordance was seen between ALI and mALI in the pre-treatment setting and this was statistically significant. A significant proportion of patients with poor ALI (90.9%), poor pre-chemotherapy mALI (91.3%) and poor post-chemotherapy mALI (89%) had poor NLR and each of them was statistically significant. Conclusions: In both univariate and multivariate analyses, this study demonstrated the statistical significance of sarcopenia, SMD, and mALI as predictive factors for OS. Additionally, sarcopenia and SMD were also found to be statistically significant factors in predicting PFS. These biomarkers could potentially help triage patients for active nutritional intervention for better outcomes.

Utility of a simplified [18F] sodium fluoride PET imaging method to quantify bone metabolic flux for a wide range of clinical applications.

We review the rationale, methodology, and clinical utility of quantitative [18F] sodium fluoride ([18F]NaF) positron emission tomography-computed tomography (PET-CT) imaging to measure bone metabolic flux (Ki, also known as bone plasma clearance), a measurement indicative of the local rate of bone formation at the chosen region of interest. We review the bone remodelling cycle and explain what aspects of bone remodelling are addressed by [18F]NaF PET-CT. We explain how the technique works, what measurements are involved, and what makes [18F]NaF PET-CT a useful tool for the study of bone remodelling. We discuss how these measurements can be simplified without loss of accuracy to make the technique more accessible. Finally, we briefly review some key clinical applications and discuss the potential for future developments. We hope that the simplified method described here will assist in promoting the wider use of the technique.

Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management.

Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.

Response assessment of post-treatment head and neck cancers to determine further management using NI-RADS (Neck Imaging Reporting and Data System): a subgroup analysis of a randomized controlled trial.

Objective: Interpreting complex post-treatment changes in head and neck cancer (HNC) is challenging with further added perplexity due to variable interobserver interpretation and hence evolved the NI-RADS lexicon. We evaluated the accuracy of NI-RADS in predicting disease status on 1st post-treatment follow-up CECT in a homogenous cohort of those who received only chemoradiation. Methods: Retrospective analysis of imaging was done for LASHNC patients who received radical chemoradiation in an open-label, investigator-initiated, phase 3 randomized trial (2012-2018) randomly assigned to either radical radiotherapy with concurrent weekly cisplatin (CRT) or CRT with the same schedule plus weekly nimotuzumab (NCRT). 536 patients were accrued, and 74 patients who did not undergo PET/CECT after 8 weeks post-CRT were excluded. After assessing 462 patients for eligibility to allocate NI-RADS at primary and node sites, 435 cases fell in the Primary disease cohort and 412 cases in the Node disease cohort. We evaluated sensitivity, disease prevalence, the positive and negative predictive value of the NI-RADS lexicon, and accuracy, which were expressed as percentages. We also prepared flow charts to determine concordance with allocated NI-RADS category and established accuracy with which it can identify disease status. Results: Out of 435 primary disease cohort, 92%, 55%, 48%,70% were concordant and had 100%, 72%, 70%, 82% accuracy in NI-RADS1 (n=12), NI-RADS2 (n=261), NIRADS3 (n=105), and NI-RADS 4 (n=60) respectively. Out of 412 nodes disease cohort, 95%, 90%, 48%, 70%were concordant and had 92%, 97%, 90%, 67% accuracy in NI-RADS1 (n=57), NI-RADS2 (n=255), NI-RADS3 (n=105) and NI-RADS4 (n=60) respectively. % concordance of PET/CT and CECT across all primary and node disease cohorts revealed that PET/CT was 91% concordant in primary NI-RADS2 as compared to 55% concordance of CECT whereas concordance of CECT was better with 57% in primary NI-RADS3 cohort as compared to PET/CT concordance of 41%. Conclusion: The accuracy with which the NI-RADS lexicon performed in our study at node sites was better than that at the primary site. There is a great scope of research to understand if CECT performs better over clinical disease status in NI-RADS 3 and 4 categories. Further research should be carried out to understand if PET/CECT can be used for close interval follow-up in stage III/IV NI-RADS 2 cases.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction.

Aim: Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods: This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results: Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)]. Conclusions: The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

PURPOSE: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.