Myosteatosis predicts survival after surgery for periampullary cancer::a novel method using MRI

Background: Myosteatosis, characterized by inter-and intramyocellular fat deposition, is strongly related to poor overall survival after surgery for periampullary cancer. It is commonly assessed by calculating the muscle radiation attenuation on computed tomography (CT) scans. However, since magnetic resonance imaging (MRI) is replacing CT in routine diagnostic work-up, developing methods based on MRI is important. We developed a new method using MRI-muscle signal intensity to assess myosteatosis and compared it with CT-muscle radiation attenuation.Methods: Patients were selected from a prospective cohort of 236 surgical patients with periampullary cancer. The MRI-muscle signal intensity and CT-muscle radiation attenuation were assessed at the level of the third lumbar vertebra and related to survival.Results: Forty-seven patients were included in the study. Inter-observer variability for MRI assessment was low (R-2 = 0.94). MRI-muscle signal intensity was associated with short survival: median survival 9.8 (95%-CI: 1.5-18.1) vs. 18.2 (95%-CI: 10.7-25.8) months for high vs. low intensity, respectively (p = 0.038). Similar results were found for CT-muscle radiation attenuation (low vs. high radiation attenuation: 10.8 (95%-CI: 8.5-13.1) vs. 15.9 (95%-CI: 10.2-21.7) months, respectively; p = 0.046). MRI-signal intensity correlated negatively with CT-radiation attenuation (r=-0.614, p &lt;0.001).Conclusions: Myosteatosis may be adequately assessed using either MRI-muscle signal intensity or CT-muscle radiation attenuation.</p

Mutational Analysis Identifies Therapeutic Biomarkers in Inflammatory Bowel Disease-Associated Colorectal Cancers.

Purpose: Inflammatory bowel disease-associated colorectal cancers (IBD-CRC) are associated with a higher mortality than sporadic colorectal cancers. The poorly defined molecular pathogenesis of IBD-CRCs limits development of effective prevention, detection, and treatment strategies. We aimed to identify biomarkers using whole-exome sequencing of IBD-CRCs to guide individualized management.Experimental Design: Whole-exome sequencing was performed on 34 formalin-fixed paraffin-embedded primary IBD-CRCs and 31 matched normal lymph nodes. Computational methods were used to identify somatic point mutations, small insertions and deletions, mutational signatures, and somatic copy number alterations. Mismatch repair status was examined.Results: Hypermutation was observed in 27% of IBD-CRCs. All hypermutated cancers were from the proximal colon; all but one of the cancers with hypermutation had defective mismatch repair or somatic mutations in the proofreading domain of DNA POLE Hypermutated IBD-CRCs had increased numbers of predicted neo-epitopes, which could be exploited using immunotherapy. We identified six distinct mutation signatures in IBD-CRCs, three of which corresponded to known mechanisms of mutagenesis. Driver genes were also identified.Conclusions: IBD-CRCs should be evaluated for hypermutation and defective mismatch repair to identify patients with a higher neo-epitope load who may benefit from immunotherapies. Prospective trials are required to determine whether IHC to detect loss of MLH1 expression in dysplastic colonic tissue could identify patients at increased risk of developing IBD-CRC. We identified mutations in genes in IBD-CRCs with hypermutation that might be targeted therapeutically. These approaches would complement and individualize surveillance and treatment programs. Clin Cancer Res; 24(20); 5133-42. ©2018 AACR

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

peer reviewedDetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours

Biomarkers for hypoxia, hpvness, and proliferation from imaging perspective

Recent advances in quantitative imaging with handcrafted radiomics and unsupervised deep learning have resulted in a plethora of validated imaging biomarkers in the field of head and neck oncology. Generally speaking, these algorithms are trained for one specific task, e.g. to classify between two or multiple types of underlying tumor biology (e.g. hypoxia, HPV status), predict overall survival (OS) or progression free survival (PFS), automatically segment a region of interest e.g. an organ at risk for radiotherapy dose or the gross tumor volume (GTV). Despite relatively good performances in external validation cohorts these algorithms still have not found their way into routine clinical practice. The reason this has not happened yet is complex, multifactorial, and can be usually divided into three categories: technical (a part of the algorithm or pre-processing step is not technically sound), statistical (mainly related to selection of subset of relevant biomarkers), and translational (not enough understanding by clinicians, not easily implementable within clinical workflow). We currently foresee that the next artificial intelligence (AI)-driven technique to find its way into clinical practice beside existing techniques (e.g. automatic organ at risk segmentation) will be the automatic segmentation of head and neck gross tumor volumes

Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients

Purpose The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status. Method 209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed. Results Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75−0.86) and 0.76 (0.71−0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6−0.82) using cubic interpolation and 0.72 (0.6−0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation). Conclusions MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation

Tracking tumor biology with radiomics:A systematic review utilizing a radiomics quality score

Introduction: In this review we describe recent developments in the field of radiomics along with current relevant literature linking it to tumor biology. We furthermore explore the methodologic quality of these studies with our in-house radiomics quality scoring (RQS) tool. Finally, we offer our vision on necessary future steps for the development of stable radiomic features and their links to tumor biology. Methods: Two authors (S.S. and H.W.) independently performed a thorough systematic literature search and outcome extraction to identify relevant studies published in MEDLINE/PubMed (National Center for Biotechnology Information, NCBI), EMBASE (Ovid) and Web of Science (WoS). Two authors (S.S, H.W) separately and two authors (J.v.T and E.d.J) concordantly scored the articles for their methodology and analyses according to the previously published radiomics quality score (RQS). Results: In summary, a total of 655 records were identified till 25-09-2017 based on the previously specified search terms, from which n = 236 in MEDLINE/PubMed, n = 215 in EMBASE and n = 204 from Web of Science. After determining full article availability and reading the available articles, a total of n = 41 studies were included in the systematic review. The RQS scoring resulted in some discrepancies between the reviewers, e.g. reviewer H.W scored 4 studies &gt;= 50%, reviewer S.S scored 3 studies &gt;= 50% while reviewers J.v.T and E.d.J scored 1 study &gt;= 50%. Up to nine studies were given a quality score of 0%. The majority of studies were scored below 50%. Discussion: In this study, we performed a systematic literature search linking radiomics to tumor biology. All but two studies (n = 39) revealed that radiomic features derived from ultrasound, CT, PET and/or MR are significantly associated with one or several specific tumor biologic substrates, from somatic mutation status to tumor histopathologic grading and metabolism. Considerable inter-observer differences were found with regard to RQS scoring, while important questions were raised concerning the interpretability of the outcome of such scores. (C) 2018 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 127 (2018) 349-360This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)