Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.
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| Title: | Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study. |
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| Authors: | Lee, Taehee1 (AUTHOR), Lee, Kyung Hee2,3 (AUTHOR), Lee, Jong Hyuk1,2 (AUTHOR), Park, Samina4 (AUTHOR), Kim, Young Tae4,5 (AUTHOR), Goo, Jin Mo1,2,5,6 (AUTHOR), Kim, Hyungjin1,2 (AUTHOR) khj.snuh@gmail.com |
| Source: | European Radiology. May2024, Vol. 34 Issue 5, p3431-3443. 13p. |
| Subjects: | Deep learning, Receiver operating characteristic curves, Lungs, Adenocarcinoma, Histopathology, Signal convolution |
| Abstract: | Objectives: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. Methods: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. Results: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01–1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002–1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005–1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). Conclusion: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. Clinical relevance statement: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. Key Points: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13). [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 177463555 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Taehee%22">Lee, Taehee</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Kyung+Hee%22">Lee, Kyung Hee</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Jong+Hyuk%22">Lee, Jong Hyuk</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Samina%22">Park, Samina</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Young+Tae%22">Kim, Young Tae</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goo%2C+Jin+Mo%22">Goo, Jin Mo</searchLink><relatesTo>1,2,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Hyungjin%22">Kim, Hyungjin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> khj.snuh@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. May2024, Vol. 34 Issue 5, p3431-3443. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Lungs%22">Lungs</searchLink><br /><searchLink fieldCode="DE" term="%22Adenocarcinoma%22">Adenocarcinoma</searchLink><br /><searchLink fieldCode="DE" term="%22Histopathology%22">Histopathology</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+convolution%22">Signal convolution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. Methods: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. Results: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01–1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002–1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005–1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). Conclusion: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. Clinical relevance statement: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. Key Points: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-023-10306-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 3431 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Receiver operating characteristic curves Type: general – SubjectFull: Lungs Type: general – SubjectFull: Adenocarcinoma Type: general – SubjectFull: Histopathology Type: general – SubjectFull: Signal convolution Type: general Titles: – TitleFull: Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Taehee – PersonEntity: Name: NameFull: Lee, Kyung Hee – PersonEntity: Name: NameFull: Lee, Jong Hyuk – PersonEntity: Name: NameFull: Park, Samina – PersonEntity: Name: NameFull: Kim, Young Tae – PersonEntity: Name: NameFull: Goo, Jin Mo – PersonEntity: Name: NameFull: Kim, Hyungjin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: European Radiology Type: main |
| ResultId | 1 |
