Rajat Thawani

Radiomics and radiogenomics in lung cancer: A review for the clinician

Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms. The quantitative features analyzed express subvisual characteristics of images which correlate with pathogenesis of diseases. These features are broadly classified into four categories: intensity, structure, texture/gradient, and wavelet, based on the types of image attributes they capture. Many studies have been done to show correlation between these features and the malignant potential of a nodule on a chest CT. In cancer patients, these nodules also have features that can be correlated with prognosis and mutation status. The major limitations of radiomics are the lack of standardization of acquisition parameters, inconsistent radiomic methods, and lack of reproducibility. Researchers are working on overcoming these limitations, which would make radiomics more acceptable in the medical community.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Abstract. Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training (N  =  139) and the other (N  =  56) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings

Early identification of PCa patients at risk for biochemical recurrence (BCR) post-therapy will potentially complement definitive therapy with either neo- or adjuvant therapy to improve prognosis. BCR post definitive therapy is often associated with disease progression that might cause a bulge in the prostate gland. In this work we explored if an atlas-based comparison approach reveals shape differences in the prostate capsule as observed on pre-treatment T2-weighted MRI between prostate cancer patients who do (BCR+) and do not (BCR−) have BCR following definitive therapy. A single center IRB approved study included 874 patients. Complete image datasets, clinically localized PCa, availability of Gleason score, data available for post-treatment PSA and follow-up for at least 3 years in patients without BCR were the inclusion criteria to select 77 patients out of the 874 patients. Further controlling for Gleason score, stage, age and to maintain equal number of cases for the BCR+ and BCR− categories, the total number of cases was reduced to 50. Manually segmented prostate capsules were aligned to a BCR− template for statistical comparison between the BCR+ and BCR− groups. Statistically significant shape difference between the two groups was observed towards the lateral and the posterior sides of prostate.

Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI

Aggressive cancers are known to induce field effect that affect large areas of cells at a tissue surface. This means that local deformation induced by the tumor as it grows could cause distensions in regions distant from the tumor, presumably even the surface of the organ within which the tumor is growing. In this work, we focused on evaluating whether more and less aggressive prostate cancers (i.e. tumors that subsequently resulted in disease recurrence or not) could differentially induce changes and distensions in the surface of the prostate capsule. Specifically we have developed the concept of a new imaging marker called FOrge features, that attempts to quantify the degree and nature of the deformation induced in the capsule surface on account of tumor growth and then sought to evaluate whether FOrge is predictive of the risk of biochemical recurrence in prostate cancer patients based off a pre-operative T2w MRI scan. The FOrge features were extracted from a spatially contextual surface of interest (SOI) of the prostate capsule, uniquely determined from statistically significant shape differences between prostate atlases constructed from patients who did (BCR+) and who did not (BCR−) undergo biochemical recurrence. A random forest classifier trained on the FOrge features extracted from atlas images (25 BCR+ and 25 BCR−) yielded an accuracy of 78% and an AUC of 0.72 in an independent validation set of 30 patients.