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.
Abstract PURPOSE Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients. METHODS We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( >90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- (< 14mts) and long-survivors ( >14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC). RESULTS Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77). CONCLUSION Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning. *Indicates equal authorship.
Introduction: ROS1 oncogenic fusion, which was first identified by Rikova et al, is reported to be present in 1%-2% of non-small cell lung cancers (NSCLCs) and is defined as a distinct molecular sub-group.Crizotinib is very effective in ROS1-positive patients and is now Food and Drug Administration (FDA) approved for the treatment of patients with advanced ROS1-positive NSCLC.We report our experience in a tertiary cancer care hospital in India in ROS-1 positive patients. Materials and method:The present series is a retrospective analysis of 22 patients from the prospectively maintained lung cancer audit.Demographic data were collected which included age, performance status, gender, stage, co-morbidities, sites of metastasis and smoking history.Data were also collected regarding the source of financing for crizotinib whether self-financed, through insurance or Non-Governmental Organisation (NGO) sponsored.Patients who had tested negative for epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) and were subsequently found to be ROS1-mutation negative by fluorescence in situ hybridization (FISH) were evaluated on similar lines.All the data were entered and statistical analyses were performed using the SPSS software version 22.0.Response evaluation was done by RECIST 1.1 criteria. Results:Between January 2015 and December 2017, there were 22 patients who were ROS1 positive from a total of 535 patients in whom ROS1 testing was performed.A total of 16 patients could receive crizotinib and 6 patients were never exposed to crizotinib.Among the 16 patients who received crizotinib, 2 (12.5%) achieved complete response (CR) as their best response and continue to remain in CR at follow-up.13 (81%) had a partial response as best response and of which on follow-up 5 (38%) have progressed, while 8 (62%) continue to maintain response.The patients who were on crizotinib had good tolerance with none experiencing any grade 3/4 toxicity.The median follow-up of the entire cohort was 15.2 months in ROS1-positive cohort and 11.4 months in ROS1-negative cohort.In ROS1-positive cohort www.ecancer.orgecancer 2019, 13:900 median, progression-free survival (PFS) was not reached and the estimated 2-year PFS was 54% and in ROS1-negative cohort, it was 5.1 months.The median overall survival of the entire ROS1-positive cohort was not reached and the estimated 1-and 2-year overall survival (OS) was 72% and 54%, respectively, and was 8.8 months in ROS1-negative cohort. Conclusion:ROS1 rearrangement with an incidence of 4% of lung adenocarcinoma which is EGFR and ALK negative represents an important targetable driver mutation in the Indian population.Crizotinib also represents an effective treatment option with outcomes similar to those reported.Access to treatment remains an important roadblock to improve outcomes but innovative methods may improve access to these drugs.
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