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Interpretable PET/CT Radiomic Based Prognosis Modeling of NSCLC Recurrent Following Complete Resection
the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
the Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
the Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. (Digital Futures)ORCID iD: 0000-0003-2638-6047
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2022 (English)In: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

This study aimed to develop an interpretable prognostic model with a nomogram for Non-Small Cell Lung Cancer (NSCLC) recurrence prediction following complete resection, using multi-modality PET/CT fusion radiomics and patients' clinical features. Retrospectively, 181 NSCLC patients who had undergone18F-FDG PET/CT scan were enrolled and split into training (2/3) and testing (1/3) partitions. Before image fusion, PET and CT images were registered, resized to equal isotropic voxel size, and clipped and normalized. Guided Filtering Fusion GFF algorithm was used for image fusion. Two hundred eighteen radiomic features were extracted from each PET, CT, and fused image, including morphological, first-order statistical, and texture features. Clinical features included age, sex, smoking status, weight, radiation, chemotherapy, pathological stage, etc. Feature selection and univariate and multivariate modeling were performed using the CoxBoost algorithm. Harrell's Concordance index (C-index) was used to evaluate the performance of the models, and compare C test was used to statistically compare the performance of the models (p-values < 0.05 were considered significant). Clinical, Clinical+PET, Clinical+CT, and Clinical+GFF resulted in c-indices (confidence interval) of 0.701 (0.589-0.812), 0.757 (0.647-0.867), 0.706 (0.607, 0.807), and 0.824 (0.751-0.896), respectively. Statistical comparison of the performance of different models with the Clinical model revealed that while PET and GFF features can significantly increase the performance (p-values 0.009 and 0.001, respectively), CT features did not significantly improve the performance of the Clinical model (p-value 0.279). Therefore, the nomogram was developed based on the Clinical+GFF model (with the best performance). Radiomic features extracted from PET and PET/CT fusion images can improve the recurrence prognosis in NSCLC patients compared to the conventional clinical factors alone.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022.
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-344025DOI: 10.1109/NSS/MIC44845.2022.10399219Scopus ID: 2-s2.0-85185373982ISBN: 978-1-6654-8872-3 (print)OAI: oai:DiVA.org:kth-344025DiVA, id: diva2:1841395
Conference
2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022, Milano, Italy, Nov 5 2022 - Nov 12 2022
Note

QC 20240229

Part of ISBN 978-166548872-3

Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-29Bibliographically approved

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Chatterjee, Saikat

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