Richard A. D. Carano

PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For ¹⁸ F-fluorodeoxyglucose–avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic. METHODS: Here, we present a deep learning–based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741 ) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers NCT02257567 , NCT02500407 ; 20% holdout in ClinicalTrials.gov identifier NCT01287741 ), outputs the detected lesions at baseline and follow-up to enable focused radiologist review. RESULTS: The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers NCT01287741 , NCT02500407 , and NCT02257567 , respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers NCT01287741 , NCT02500407 , and NCT02257567 , compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses. CONCLUSION: These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.

Segmenting MRI T1 unenhancing and contrast-enhancing multiple sclerosis lesions jointly using deep learning models from OPERA clinical trials improved contrast-enhancing lesion segmentation and det... Background Deep learning–based segmentation could facilitate rapid and reproducible T1 lesion load assessments, which is crucial for disease management in multiple sclerosis (MS). T1 unenhancing an...

Despite the development of effective therapies, a substantial proportion of asthmatics continue to have uncontrolled symptoms, airflow limitation, and exacerbations. Transient receptor potential cation channel member A1 (TRPA1) agonists are elevated in human asthmatic airways, and in rodents, TRPA1 is involved in the induction of airway inflammation and hyperreactivity. Here, the discovery and early clinical development of GDC-0334, a highly potent, selective, and orally bioavailable TRPA1 antagonist, is described. GDC-0334 inhibited TRPA1 function on airway smooth muscle and sensory neurons, decreasing edema, dermal blood flow (DBF), cough, and allergic airway inflammation in several preclinical species. In a healthy volunteer Phase 1 study, treatment with GDC-0334 reduced TRPA1 agonist-induced DBF, pain, and itch, demonstrating GDC-0334 target engagement in humans. These data provide therapeutic rationale for evaluating TRPA1 inhibition as a clinical therapy for asthma.

Purpose: The tumor microenvironment presents with altered extracellular matrix (ECM) and stroma composition, which may affect treatment efficacy and contribute to tissue stiffness. Ultrasound (US) elastography can visualize and quantify tissue stiffness noninvasively. However, the contributions of ECM and stromal components to stiffness are poorly understood. We therefore set out to quantify ECM and stroma density and their relation to tumor stiffness. Experimental Design: A modified clinical ultrasound system was used to measure tumor stiffness and perfusion during tumor growth in preclinical tumor models. In vivo measurements were compared with collagen mass spectroscopy and automatic analysis of matrix and stromal markers derived from immunofluorescence images. Results: US elastography estimates of tumor stiffness were positively correlated with tumor volume in collagen and myofibroblast-rich tumors, while no correlations were found for tumors with low collagen and myofibroblast content. US elastography measurements were strongly correlated with ex vivo mechanical testing and mass spectroscopy–based measurements of total collagen and immature collagen crosslinks. Registration of ultrasound and confocal microscopy data showed strong correlations between blood vessel density and T-cell density in syngeneic tumors, while no correlations were found for genetic tumor models. In contrast to collagen density, which was positively correlated with stiffness, no significant correlations were observed for hyaluronic acid density. Finally, localized delivery of collagenase led to a significant reduction in tumor stiffness without changes in perfusion 24 hours after treatment. Conclusions: US elastography can be used as a potential biomarker to assess changes in the tumor microenvironment, particularly changes affecting the ECM. Clin Cancer Res; 24(18); 4455–67. ©2018 AACR.