High-resolution computed tomography (HRCT) plays an essential role in the evaluation of patients with Interstitial Lung Disease (ILD) such as Idiopathic Pulmonary Fibrosis (IPF), Connective Tissue Disease (CTD)-ILD and Progressive Pulmonary Fibrosis (PPF). Computational methods for quantitative evaluation of HRCT have emerged as promising objective markers of disease severity in pulmonary fibrosis. These quantitative methods capitalize on detailed information available on images and have the potential to increase precision and reproducibility in the evaluation of lung fibrosis on CT images.
HRCT-derived scores for fibrosis extent correlate with the degree of physiological impairment at baseline and may be more sensitive to subtle changes in disease status than physiological metrics. In clinical trials, potential application of quantitative CT includes, stratification of baseline severity of disease, early detection of fibrosis and assessment of progression or improvement in response to treatment.
In this webinar, Calyx’s Dr. Rohit Sood – an ILD trialist with over a decade of experience in researching and developing imaging biomarkers for lung fibrosis – will lead a discussion with leading pulmonary imaging experts on AI-driven strategies for quantitative CT-based assessment in clinical trials of pulmonary fibrosis. Dr. David Lynch will review the ILD landscape and current challenges associated with the qualitative and semi-quantitative review of HRCT images in ILD patients. Dr. Stephen Humphries will present a Deep Learning-based DTA method, developed in his lab for the quantification of lung fibrosis and assessment of treatment response in pharmaceutical clinical trials.
时间
2023 年 9 月 27 日
10AM-11AM ET
发言人
Rohit Sood
Vice President, Scientific & Medical Services
Rohit Sood MD., PhD. is the Vice President of Scientific and Medical Services at Calyx. In his present role as the Emerging Therapeutics Lead, which covers Cardio-Thoracic, CNS, GI, Rare diseases and Advanced Technologies, Dr. Sood has contributed as a consultant/scientific lead on sponsored 160+ imaging clinical trials spanning the entire spectrum, from early to late phase, across multiple therapeutic areas. He was responsible for supporting 20+ drug approval clinical studies that involved imaging as a key trial endpoint. Over the past decade, he has been instrumental in developing partnerships with academic and non-academic labs to leverage Machine and Deep Learning methodologies for application in pharmaceutical drug studies that involve imaging based endpoints. More specifically, using ML/DL methods to analyze images and develop quantitative biomarkers for use as endpoints in clinical trials. The usefulness of some of these biomarkers was evaluated in clinical trials and results were published in peer reviewed scientific journals.
