EGFR Mutations in Indian Lung Cancer Patients: Clinical Correlation and Outcome to EGFR Targeted Therapy

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Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features

Needle(s) in the Haystack – Synchronous Multifocal Tumor Induced Osteomalacia

This is the author accepted manuscript. The final version is available from Endocrine Society via http://dx.doi.org/10.1210/jc.2015-3854MG is supported by the NIHR Cambridge Biomedical Research Centre

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

Imaging of lung cancer: Implications on staging and management

Lung cancer is one of the leading causes of cancer-related deaths. Accurate assessment of disease extent is important in deciding the optimal treatment approach. To play an important role in the multidisciplinary management of lung cancer patients, it is necessary that the radiologist understands the principles of staging and the implications of radiological findings on the various staging descriptors and eventual treatment decisions

An Unusual Cause of the Ring Artifact on Transaxial CT Images

Artifacts and image quality are two sides of the same coin. The ring artifact is scanner-based and caused mainly by either a miscalibrated element or a defective element of a detector row. We describe a rare cause of the ring artifact that appeared on a transaxial CT image because of a loose electronic contact. To our knowledge, this particular cause of the ring artifact has not been described in literature. </p

A pictoral review on somatostatin receptor scintigraphy in neuroendocrine tumors: The role of multimodality imaging with SRS and GLUT receptor imaging with FDG PET-CT

Somatostatin receptor scintigraphy is considered as a comprehensive imaging modality for many neuroendocrine tumors. Multiple radiotracers using combinations of gamma or positron emitting radionuclides and tracers are now available. Newer radiopharmaceuticals using 99m Tc labeled with TOC, TATE, NOC are good alternatives to the 68 - Gallium radiotracers where the PET facility is not available. The pictoral depicts the role of SRS using 99mTC - HYNIC -TOC radiotracers in staging and treatment planning of NETs. Characterization of the tumor biology using combined SRS and FDG PET/CT is also demonstrated with a proposed categorization method. The emerging role of SRS in tailored targeted radionuclide therapy is outlined in brief

A rare variant of Caffey's disease – X-rays, bone scan and FDG PET findings

An 18-month-old boy with history of fever of 4 months duration and with swelling of the limbs was referred for a bone scan. There were multiple swellings over his upper and lower limbs, with bowing of the lower limbs. His radiological skeletal survey revealed marked periosteal new bone formation surrounding the diaphysis of long bones. A bone scan done with 99m Tc-MDP showed diffusely increased tracer uptake in all the long bones. A fluorodeoxyglucose positron emission tomography (FDG PET) scan done to assess the metabolic activity showed patchy FDG uptake in the long bones, ankle joint and anterior ends of few ribs. His clinical and imaging findings led to the diagnosis of Caffey's disease