Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori

Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning

Purpose Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis. Methods and Materials Ninety patients treated for adjuvant whole-breast RT (4250 cGy/fx = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at fx = 5, fx = 10, and fx = 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at fx = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set. Results Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at fx = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at fx = 5 for predicting skin toxicity at the end of RT. Conclusions Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

The Association of COG5 and SRD5A3 Gene Mutations to ER Stress Activation and their Role in Retinal Degeneration

Five individuals were diagnosed with Leber’s congenital amaurosis, an early-onset inherited vision disorder. Two unrelated individuals have a homozygous mutation in SRD5A3 (p.W19X). The remaining three are siblings that have compound heterozygous mutations in COG5 (p.M32R; p.S777Q_fs*14). Both SRD5A3 and COG5 play critical roles in glycosylation, which if disrupted could result in ER stress. Transfection of wild-type or mutant variants of SRD5A3 or COG5 in vitro found no link between SRD5A3 and ER stress. However, PERK was significantly upregulated in response to compound transfection of the mutant COG5 variants. Immunocytochemical staining revealed that the compound COG5 mutants lead to a significant increase in Golgi fragmentation and DNA damage. Treatment of the transfected cells with PERK inhibitor, GSK2606414, reduced DNA damage levels to that of the control. This study is the first to associate COG5 disruption with ER stress and has identified a potential therapeutic target for patients with COG5-related disorders.M.Sc.2019-11-15 00:00:0

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining “big data”). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring