1
|
Kulkarni C, Sherkhane U, Jaiswar V, Mithun S, Mysore Siddu D, Rangarajan V, Dekker A, Traverso A, Jha A, Wee L. Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital. BJR Open 2024; 6:tzad008. [PMID: 38352184 PMCID: PMC10860512 DOI: 10.1093/bjro/tzad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/15/2023] [Accepted: 11/20/2023] [Indexed: 02/16/2024] Open
Abstract
Objectives Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting. In this work, we tested the performance of DL automatic lung GTV segmentation model trained on open-access Dutch data when used on Indian patients from a large public tertiary hospital, and hypothesized that generic DL performance could be improved for a specific local clinical context, by means of modest transfer-learning on a small representative local subset. Methods X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a local tertiary hospital (Test Set 2). Another Indian data set (Retrain Set 1) was used to fine-tune the former DL model using a transfer learning method. The Indian data sets were taken from CT of a hybrid scanner based in nuclear medicine, but the GTV was drawn by skilled Indian ROs. The final (after fine-tuning) model (Model 2) was then re-evaluated in "Interobserver1" and "Test Set 2." Dice similarity coefficient (DSC), precision, and recall were used as geometric segmentation performance metrics. Results Model 1 trained exclusively on Dutch scans showed a significant fall in performance when tested on "Test Set 2." However, the DSC of Model 2 recovered by 14 percentage points when evaluated in the same test set. Precision and recall showed a similar rebound of performance after transfer learning, in spite of using a comparatively small sample size. The performance of both models, before and after the fine-tuning, did not significantly change the segmentation performance in "Interobserver1." Conclusions A large public open-access data set was used to train a generic DL model for lung GTV segmentation, but this did not perform well initially in the Indian clinical context. Using transfer learning methods, it was feasible to efficiently and easily fine-tune the generic model using only a small number of local examples from the Indian hospital. This led to a recovery of some of the geometric segmentation performance, but the tuning did not appear to affect the performance of the model in another open-access data set. Advances in knowledge Caution is needed when using models trained on large volumes of international data in a local clinical setting, even when that training data set is of good quality. Minor differences in scan acquisition and clinician delineation preferences may result in an apparent drop in performance. However, DL models have the advantage of being efficiently "adapted" from a generic to a locally specific context, with only a small amount of fine-tuning by means of transfer learning on a small local institutional data set.
Collapse
Affiliation(s)
- Chaitanya Kulkarni
- Philips Research, Philips Innovation Campus, Bengaluru, Karnataka 560045, India
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
| | - Umesh Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
- Department of Nuclear Medicine and Radiology, Tata Memorial Hospital Mumbai, Mumbai, Maharashtra 400012, India
| | - Vinay Jaiswar
- Department of Nuclear Medicine and Radiology, Tata Memorial Hospital Mumbai, Mumbai, Maharashtra 400012, India
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
- Department of Nuclear Medicine and Radiology, Tata Memorial Hospital Mumbai, Mumbai, Maharashtra 400012, India
| | - Dinesh Mysore Siddu
- Philips Research, Philips Innovation Campus, Bengaluru, Karnataka 560045, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Radiology, Tata Memorial Hospital Mumbai, Mumbai, Maharashtra 400012, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
- Faculty of Medicine, University Vita Salute, San Raffaele Hospital, 20132 Milan, Italy
| | - Ashish Jha
- Department of Nuclear Medicine and Radiology, Tata Memorial Hospital Mumbai, Mumbai, Maharashtra 400012, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht 6229 ET, The Netherlands
| |
Collapse
|