{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113224"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113224","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Lung cancer malignancy predication with recurrent neural networks","abstract":"Lung cancer has the highest mortality rate among all cancer types in the United States, comprising almost 25% of all cancer deaths. Existing work in computer-aided diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted that advise high-risk patients to get annual CT screenings to monitor any suspicious lesions found in the lungs. This change increases the availability of CT scans and the number of scans per patient for computational models to learn from. In this thesis, we present bounding box annotations for a subset of patients from the National Lung Screening Trial (NLST) over three years time and provide baseline results on the benchmark task of malignancy prediction using this time-series data. We analyze the use of longitudinal models to capture the progression of nodule malignancy and see that recurrent neural networks (RNNs) outperform standard CNNs by 4.58% in accuracy, 5.03% in precision for a fixed sensitivity of 95.06%, and 6.61% in area under the curve (AUC).","abstract_html":"Lung cancer has the highest mortality rate among all cancer types in the United States, comprising almost 25% of all cancer deaths. Existing work in computer-aided diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted that advise high-risk patients to get annual CT screenings to monitor any suspicious lesions found in the lungs. This change increases the availability of CT scans and the number of scans per patient for computational models to learn from. In this thesis, we present bounding box annotations for a subset of patients from the National Lung Screening Trial (NLST) over three years time and provide baseline results on the benchmark task of malignancy prediction using this time-series data. We analyze the use of longitudinal models to capture the progression of nodule malignancy and see that recurrent neural networks (RNNs) outperform standard CNNs by 4.58% in accuracy, 5.03% in precision for a fixed sensitivity of 95.06%, and 6.61% in area under the curve (AUC).","abstract_has_math":false,"creators":["Dasso, Mary Kathleen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:35:21Z","date_published":"2022-01-12T22:35:21Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Lung cancer","malignancy prediction","recurrent neural networks","deep learning"],"languages":["en"],"rights":["Copyright 2021 Mary Kathleen Dasso"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113224","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh"]},{"key":"dc:creator","label":"Author","values":["Dasso, Mary Kathleen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:35:21Z","2024-01-12T22:35:30Z","2021-07-21","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Lung cancer","malignancy prediction","recurrent neural networks","deep learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Mary Kathleen Dasso"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113224"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Lung cancer has the highest mortality rate among all cancer types in the United States, comprising almost 25% of all cancer deaths. Existing work in computer-aided diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted that advise high-risk patients to get annual CT screenings to monitor any suspicious lesions found in the lungs. This change increases the availability of CT scans and the number of scans per patient for computational models to learn from. In this thesis, we present bounding box annotations for a subset of patients from the National Lung Screening Trial (NLST) over three years time and provide baseline results on the benchmark task of malignancy prediction using this time-series data. We analyze the use of longitudinal models to capture the progression of nodule malignancy and see that recurrent neural networks (RNNs) outperform standard CNNs by 4.58% in accuracy, 5.03% in precision for a fixed sensitivity of 95.06%, and 6.61% in area under the curve (AUC).","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Mary Dasso, accepted the attached license on 2021-07-21 at 11:52.","The student, Mary Dasso, submitted this Thesis for approval on 2021-07-21 at 11:57.","This Thesis was approved for publication on 2021-07-21 at 14:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17051 on 2022-01-12 at 12:55:35","Made available in DSpace on 2022-01-12T22:35:21Z (GMT). No. of bitstreams: 2 DASSO-THESIS-2021.pdf: 2134304 bytes, checksum: 8ab24b85418e13f2ca95ee3523487f57 (MD5) LICENSE.txt: 4207 bytes, checksum: a390b4b7688a732b73d2f44432f45077 (MD5) Previous issue date: 2021-07-21","Embargo set by: Seth Robbins for item 121150 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Lung cancer malignancy predication with recurrent neural networks"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh"],"dc:creator":["Dasso, Mary Kathleen"],"dc:date":["2022-01-12T22:35:21Z","2024-01-12T22:35:30Z","2021-07-21","2021-08"],"dc:description":["Lung cancer has the highest mortality rate among all cancer types in the United States, comprising almost 25% of all cancer deaths. Existing work in computer-aided diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted that advise high-risk patients to get annual CT screenings to monitor any suspicious lesions found in the lungs. This change increases the availability of CT scans and the number of scans per patient for computational models to learn from. In this thesis, we present bounding box annotations for a subset of patients from the National Lung Screening Trial (NLST) over three years time and provide baseline results on the benchmark task of malignancy prediction using this time-series data. We analyze the use of longitudinal models to capture the progression of nodule malignancy and see that recurrent neural networks (RNNs) outperform standard CNNs by 4.58% in accuracy, 5.03% in precision for a fixed sensitivity of 95.06%, and 6.61% in area under the curve (AUC).","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Mary Dasso, accepted the attached license on 2021-07-21 at 11:52.","The student, Mary Dasso, submitted this Thesis for approval on 2021-07-21 at 11:57.","This Thesis was approved for publication on 2021-07-21 at 14:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17051 on 2022-01-12 at 12:55:35","Made available in DSpace on 2022-01-12T22:35:21Z (GMT). No. of bitstreams: 2 DASSO-THESIS-2021.pdf: 2134304 bytes, checksum: 8ab24b85418e13f2ca95ee3523487f57 (MD5) LICENSE.txt: 4207 bytes, checksum: a390b4b7688a732b73d2f44432f45077 (MD5) Previous issue date: 2021-07-21","Embargo set by: Seth Robbins for item 121150 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113224"],"dc:language":["en"],"dc:rights":["Copyright 2021 Mary Kathleen Dasso"],"dc:subject":["Lung cancer","malignancy prediction","recurrent neural networks","deep learning"],"dc:title":["Lung cancer malignancy predication with recurrent neural networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}