Search: onr:"swepub:oai:DiVA.org:su-201288" > A Comparative Study...
Fältnamn | Indikatorer | Metadata |
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000 | 05716naa a2200565 4500 | |
001 | oai:DiVA.org:su-201288 | |
003 | SwePub | |
008 | 220124s2021 | |||||||||||000 ||eng| | |
009 | oai:DiVA.org:kth-307346 | |
009 | oai:prod.swepub.kib.ki.se:148529787 | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2012882 URI |
024 | 7 | a https://doi.org/10.3389/fonc.2021.7373682 DOI |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3073462 URI |
024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1485297872 URI |
040 | a (SwePub)sud (SwePub)kthd (SwePub)ki | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Astaraki, Mehdi,c PhD Student,d 1984-u Karolinska Institutet,KTH,Medicinsk avbildning,Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.4 aut0 (Swepub:kth)u1usc61v |
245 | 1 0 | a A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images |
264 | c 2021-12-17 | |
264 | 1 | b Frontiers Media SA,c 2021 |
338 | a print2 rdacarrier | |
500 | a QC 20220124 | |
520 | a Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study. | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
653 | a lung nodule | |
653 | a benign-malignant classification | |
653 | a lung cancer prediction | |
653 | a radiomics | |
653 | a deep classifier | |
700 | 1 | a Yang, Guangu Royal Brompton Hosp, Cardiovasc Res Ctr, London, England.;Imperial Coll London, Natl Heart & Lung Inst, London, England.4 aut |
700 | 1 | a Zakko, Yousufu Karolinska Univ Hosp, Dept Radiol Imaging & Funct, Solna, Sweden.4 aut |
700 | 1 | a Toma-Dasu, Iulianau Stockholms universitet,Fysikum,Karolinska Institutet, Sweden,Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Stockholm Univ, Dept Phys, Stockholm, Sweden.4 aut0 (Swepub:su)iuda0736 |
700 | 1 | a Smedby, Örjan,c Professor,d 1956-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1vc2uzb |
700 | 1 | a Wang, Chunliang,d 1980-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1tbkeej |
710 | 2 | a KTHb Medicinsk avbildning4 org |
773 | 0 | t Frontiers in Oncologyd : Frontiers Media SAg 11q 11x 2234-943X |
856 | 4 | u https://doi.org/10.3389/fonc.2021.737368y Fulltext |
856 | 4 | u https://www.frontiersin.org/articles/10.3389/fonc.2021.737368/pdf |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-201288 |
856 | 4 8 | u https://doi.org/10.3389/fonc.2021.737368 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-307346 |
856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:148529787 |
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