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Proceedings Paper

RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer
Author(s): Pranjal Vaidya; Xiangxue Wang; Kaustav Bera; Arjun Khunger; Humberto Choi; Pradnya Patil; Vamsidhar Velcheti; Anant Madabhushi
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Paper Abstract

Non-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. The treatment of choice for early stage NSCLC is surgical resection followed by adjuvant chemotherapy for high risk patients. Currently, the decision to offer chemotherapy is primarily dependent on several clinical and visual radiographic factors as there is a lack of a biomarker which can accurately stratify and predict disease risk in these patients. Computer extracted image features from CT scans (radiomic) and (pathomic) from H&E tissue slides have already shown promising results in predicting recurrence free survival (RFS) in lung cancer patients. This paper presents new radiology-pathology fusion approach (RaPtomics) to combine radiomic and pathomic features for predicting recurrence in early stage NSCLC. Radiomic textural features (Gabor, Haralick, Law, Laplace and CoLlAGe) from within and outside lung nodules on CT scans and intranuclear pathology features (Shape, Cell Cluster Graph and Global Graph Features) were extracted from digitized whole slide H&E tissue images on an initial discovery set of 50 patients. The top most predictive radiomic and pathomic features were then combined and in conjunction with machine learning algorithms were used to predict classifier. The performance of the RaPtomic classifier was evaluated on a training set from the Cleveland Clinic (n=50) and independently validated on images from the publicly available cancer genome atlas (TCGA) dataset (n=43). The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) (AUC 0.78; p<0.005) as compared to radiomic (AUC 0.74; p<0.01) and pathomic (AUC 0.67; p<0.05) features alone.

Paper Details

Date Published: 6 March 2018
PDF: 11 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810M (6 March 2018); doi: 10.1117/12.2296646
Show Author Affiliations
Pranjal Vaidya, Case Western Reserve Univ. (United States)
Xiangxue Wang, Case Western Reserve Univ. (United States)
Kaustav Bera, Case Western Reserve Univ. (United States)
Arjun Khunger, Cleveland Clinic (United States)
Humberto Choi, Cleveland Clinic (United States)
Pradnya Patil, Cleveland Clinic (United States)
Vamsidhar Velcheti, Cleveland Clinic (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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