Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.Comment: IEEE VIS (SciVis) 201

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC

The impact of responding to patient messages with large language model assistance

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie

Randomized Controlled Trial of Fish Oil and Montelukast and Their Combination on Airway Inflammation and Hyperpnea-Induced Bronchoconstriction

Both fish oil and montelukast have been shown to reduce the severity of exercise-induced bronchoconstriction (EIB). The purpose of this study was to compare the effects of fish oil and montelukast, alone and in combination, on airway inflammation and bronchoconstriction induced by eucapnic voluntary hyperpnea (EVH) in asthmatics. In this model of EIB, twenty asthmatic subjects with documented hyperpnea-induced bronchoconstriction (HIB) entered a randomized double-blind trial. All subjects entered on their usual diet (pre-treatment, n = 20) and then were randomly assigned to receive either one active 10 mg montelukast tablet and 10 placebo fish oil capsules (n = 10) or one placebo montelukast tablet and 10 active fish oil capsules totaling 3.2 g EPA and 2.0 g DHA (n = 10) taken daily for 3-wk. Thereafter, all subjects (combination treatment; n = 20) underwent another 3-wk treatment period consisting of a 10 mg active montelukast tablet or 10 active fish oil capsules taken daily. While HIB was significantly inhibited (p0.017) between treatment groups; percent fall in forced expiratory volume in 1-sec was −18.4±2.1%, −9.3±2.8%, −11.6±2.8% and −10.8±1.7% on usual diet (pre-treatment), fish oil, montelukast and combination treatment respectively. All three treatments were associated with a significant reduction (p0.017) in these biomarkers between treatments. While fish oil and montelukast are both effective in attenuating airway inflammation and HIB, combining fish oil with montelukast did not confer a greater protective effect than either intervention alone. Fish oil supplementation should be considered as an alternative treatment for EIB

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

Critical evaluation of ramucirumab in the treatment of advanced gastric and gastroesophageal cancers

Hesham ElHalawani, Omar Abdel-Rahman Clinical Oncology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt Abstract: Gastric (GC) and gastroesophageal junction (GEJ) cancers are two global health problems with a relatively high mortality, particularly in the advanced stage. Inhibition of angiogenesis is now contemplated as a classic treatment preference for myriad tumor types encompassing renal cell carcinoma, non-small cell lung cancer, colorectal cancer, glioblastoma, and ovarian cancer, among others. Bevacizumab and ramucirumab have been widely investigated in GC and GEJ cancer, with some controversy about their therapeutic role. Ramucirumab is a monoclonal antibody for vascular endothelial growth factor receptor-2, with demonstrated activity both as a monotherapy and as a part of combination strategy in the management of advanced GC/GEJ cancer. In this review article, we present a critical evaluation of the preclinical and clinical data underlying the use of this drug in this indication. Moreover, we provide a spotlight on the future perspectives in systemic therapy for advanced GC/GEJ cancer. Keywords: ramucirumab, gastric cancer, gastroesophageal cance

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

International audienceThis paper presents an overview of the first HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020. The task of the challenge is the automatic segmentation of head and neck primary Gross Tumor Volume in FDG-PET/CT images, focusing on the oropharynx region. The data were collected from five centers for a total of 254 images, split into 201 training and 53 testing cases. The interest in the task was shown by the important participation with 64 teams registered and 18 team submissions. The best method obtained a Dice Similarity Coefficient (DSC) of 0.7591, showing a large improvement over our proposed baseline method with a DSC of 0.6610 as well as inter-observer DSC agreement reported in the literature (0.69). © 2021, Springer Nature Switzerland AG

Differences between planned and delivered dose for head and neck cancer, and their consequences for normal tissue complication probability and treatment adaptation

Background and purpose: Anatomical changes induce differences between planned and delivered dose. Adaptive radiotherapy (ART) may reduce these differences but the optimal implementation is insufficiently clear. The aims of this study were to quantify the difference between planned and delivered dose in HNC patients, assess the consequential difference in normal tissue complication probability (Delta NTCP) and to explore the value of Delta NTCP as an objective selection strategy for ART.Materials and methods: For 52 patients, daily doses were accumulated to estimate the delivered dose. The difference from planned dose was analyzed for CTVs and 9 organs-at-risk (OAR). Delta NTCP was calculated for xerostomia, dysphagia, parotid gland dysfunction and tube feeding dependency at 6 months. ART was deemed necessary if Delta NTCP was >5%. The positive predicted value (PPV) was calculated for identification of ART-patients by clinical judgement, and Delta NTCP at fraction 10 and 15.Results: Delta NTCP >5% was seen five times for dysphagia and twice for the other toxicities. Only 5/9 patients with any Delta NTCP >5% clinically received ART, although ART had been done for 13/52 patients (PPV: 0.38). PPV was 0.86 and 0.75 for accumulated dose at fraction 10 and 15, respectively, using a Delta NTCP cut-off for the allocation of ART of 5%. Using other Delta NTCP cut-offs did not substantially improve PPV. With this cutoff the negative predictive value was 0.93 for Delta NTCP method of fraction 10 and fraction 15, and 0.90 for clinical judgement.Conclusion: To identify patients accurately for ART, NTCP calculations based on the dose differences between planned and delivered dose at fraction 10 are superior to clinical judgement. (C) 2019 Published by Elsevier B.V.Biological, physical and clinical aspects of cancer treatment with ionising radiatio