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Sökning: WFRF:(Wang Chunliang 1980 ) > A Comparative Study...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005716naa a2200565 4500
001oai:DiVA.org:su-201288
003SwePub
008220124s2021 | |||||||||||000 ||eng|
009oai:DiVA.org:kth-307346
009oai:prod.swepub.kib.ki.se:148529787
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2012882 URI
0247 a https://doi.org/10.3389/fonc.2021.7373682 DOI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3073462 URI
0247 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 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 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
2451 0a 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 1b 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 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe
650 7a 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
7001 a Yang, Guangu Royal Brompton Hosp, Cardiovasc Res Ctr, London, England.;Imperial Coll London, Natl Heart & Lung Inst, London, England.4 aut
7001 a Zakko, Yousufu Karolinska Univ Hosp, Dept Radiol Imaging & Funct, Solna, Sweden.4 aut
7001 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
7001 a Smedby, Örjan,c Professor,d 1956-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1vc2uzb
7001 a Wang, Chunliang,d 1980-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1tbkeej
7102 a KTHb Medicinsk avbildning4 org
7730 t Frontiers in Oncologyd : Frontiers Media SAg 11q 11x 2234-943X
8564 u https://doi.org/10.3389/fonc.2021.737368y Fulltext
8564 u https://www.frontiersin.org/articles/10.3389/fonc.2021.737368/pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-201288
8564 8u https://doi.org/10.3389/fonc.2021.737368
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-307346
8564 8u http://kipublications.ki.se/Default.aspx?queryparsed=id:148529787

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