Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma
IntroductionPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant...
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| Format: | Article |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.895515/full |
| _version_ | 1811177955901046784 |
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| author | Jeremy Chang Yanan Liu Stephanie A. Saey Kevin C. Chang Hannah R. Shrader Hannah R. Shrader Kelsey L. Steckly Maheen Rajput Milan Sonka Milan Sonka Carlos H. F. Chan Carlos H. F. Chan |
| author_facet | Jeremy Chang Yanan Liu Stephanie A. Saey Kevin C. Chang Hannah R. Shrader Hannah R. Shrader Kelsey L. Steckly Maheen Rajput Milan Sonka Milan Sonka Carlos H. F. Chan Carlos H. F. Chan |
| author_sort | Jeremy Chang |
| collection | DOAJ |
| description | IntroductionPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans.MethodsA total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed.ResultsFor a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis.DiscussionThis paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC. |
| first_indexed | 2024-04-11T06:10:02Z |
| format | Article |
| id | doaj.art-b5dfeaeafa1a489c8663c173f107229b |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T06:10:02Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-b5dfeaeafa1a489c8663c173f107229b2022-12-22T04:41:19ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-12-011210.3389/fonc.2022.895515895515Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinomaJeremy Chang0Yanan Liu1Stephanie A. Saey2Kevin C. Chang3Hannah R. Shrader4Hannah R. Shrader5Kelsey L. Steckly6Maheen Rajput7Milan Sonka8Milan Sonka9Carlos H. F. Chan10Carlos H. F. Chan11Department of Surgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesIowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, IA, United StatesDepartment of Surgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesDepartment of Surgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesDepartment of Surgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesHolden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United StatesHolden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United StatesDepartment of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesIowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, IA, United StatesDepartment of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United StatesDepartment of Surgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United StatesHolden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United StatesIntroductionPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans.MethodsA total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed.ResultsFor a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis.DiscussionThis paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC.https://www.frontiersin.org/articles/10.3389/fonc.2022.895515/fullmachine learningneural networkpancreatectomypancreatic cancersurgical outcomeradiomics |
| spellingShingle | Jeremy Chang Yanan Liu Stephanie A. Saey Kevin C. Chang Hannah R. Shrader Hannah R. Shrader Kelsey L. Steckly Maheen Rajput Milan Sonka Milan Sonka Carlos H. F. Chan Carlos H. F. Chan Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma Frontiers in Oncology machine learning neural network pancreatectomy pancreatic cancer surgical outcome radiomics |
| title | Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| title_full | Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| title_fullStr | Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| title_full_unstemmed | Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| title_short | Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| title_sort | machine learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma |
| topic | machine learning neural network pancreatectomy pancreatic cancer surgical outcome radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.895515/full |
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