Previously submitted to: JMIR Medical Informatics (no longer under consideration since Sep 07, 2022)
Date Submitted: Sep 4, 2022
Open Peer Review Period: Aug 29, 2022 - Sep 7, 2022
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Multi-kernel and Multi-scale Learning Based Deep Ensemble Model for Predicting Recurrence of Non-small Cell Lung Cancer
ABSTRACT
Background:
Predicting recurrence in patients with non-small cell lung cancer (NSCLC) prior to treatment is crucial as this may guide to personalized medicine.
Objective:
In this study, we developed a deep learning-based ensemble network for recurrence prediction using a dataset of 530 NSCLC patients.
Methods:
This network assembles two-dimensional (2D) convolutional neural network (CNN) models with various input slices, scales, and convolutional kernels through a deep learning-based ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices, providing more information than a single central slice, (ii) multi-scale networks and multi-kernel networks capturing the local and peritumoral features, (iii) ensemble design, enabling final prediction to be based on various features from different networks, and (iv) the best combination of 2D CNN models comprising various inputs and model architectures.
Results:
The ensemble of five 2D CNN models, three slices, and two multi-kernel networks achieved the best performance with a high F1 score and recall of 73% and 74.15%, respectively. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D CNN models for cancer outcome prediction in the benchmark studies.
Conclusions:
The proposed model has potential as an adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.