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Zhang K, Abdoli N, Gilley P, Sadri Y, Chen X, Thai TC, Dockery L, Moore K, Mannel RS, Qiu Y. Developing a Novel Image Marker to Predict the Clinical Outcome of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients. ARXIV 2024:arXiv:2309.07087v2. [PMID: 37744460 PMCID: PMC10516116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Objective Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcome to NACT vary significantly among different subgroups. The patients with partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of the NACT at an early stage. Methods For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of the patient receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation framework was adopted for model performance assessment. Results The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. Conclusion This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.
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Affiliation(s)
- Ke Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA 73019
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
| | - Patrik Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
| | - Youkabed Sadri
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
| | - Theresa C. Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA 73104
| | - Lauren Dockery
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA 73104
| | - Kathleen Moore
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA 73104
| | - Robert S. Mannel
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA 73104
| | - Yuchen Qiu
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA 73019
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA 73019
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Zhang K, Abdoli N, Gilley P, Sadri Y, Chen X, Thai TC, Dockery L, Moore K, Mannel RS, Qiu Y. Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients. Comput Biol Med 2024; 172:108240. [PMID: 38460312 DOI: 10.1016/j.compbiomed.2024.108240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.
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Affiliation(s)
- Ke Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA, 73019; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Patrik Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Youkabed Sadri
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019
| | - Theresa C Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Lauren Dockery
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Kathleen Moore
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Robert S Mannel
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 73104
| | - Yuchen Qiu
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA, 73019; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA, 73019.
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Yu Y, Chen Z, Zhuang Y, Yi H, Han L, Chen K, Lin J. A guiding approach of Ultrasound scan for accurately obtaining standard diagnostic planes of fetal brain malformation. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1243-1260. [PMID: 36155489 DOI: 10.3233/xst-221278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Standard planes (SPs) are crucial for the diagnosis of fetal brain malformation. However, it is very time-consuming and requires extensive experiences to acquire the SPs accurately due to the large difference in fetal posture and the complexity of SPs definitions. OBJECTIVE This study aims to present a guiding approach that could assist sonographer to obtain the SPs more accurately and more quickly. METHODS To begin with, sonographer uses the 3D probe to scan the fetal head to obtain 3D volume data, and then we used affine transformation to calibrate 3D volume data to the standard body position and established the corresponding 3D head model in 'real time'. When the sonographer uses the 2D probe to scan a plane, the position of current plane can be clearly show in 3D head model by our RLNet (regression location network), which can conduct the sonographer to obtain the three SPs more accurately. When the three SPs are located, the sagittal plane and the coronal planes can be automatically generated according to the spatial relationship with the three SPs. RESULTS Experimental results conducted on 3200 2D US images show that the RLNet achieves average angle error of the transthalamic plane was 3.91±2.86°, which has a obvious improvement compared other published data. The automatically generated coronal and sagittal SPs conform the diagnostic criteria and the diagnostic requirements of fetal brain malformation. CONCLUSIONS A guiding scanning method based deep learning for ultrasonic brain malformation screening is firstly proposed and it has a pragmatic value for future clinical application.
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Affiliation(s)
- Yalan Yu
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Zhong Chen
- Department of US, General Hospital of Western Theater Command, Chengdu, China
| | - Yan Zhuang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Heng Yi
- Department of US, General Hospital of Western Theater Command, Chengdu, China
| | - Lin Han
- College of Biomedical Engineering, Sichuan University, Chengdu, China
- Haihong Intellimage Medical Technology (Tianjin) Co., Ltd, Tianjin, China
| | - Ke Chen
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Jiangli Lin
- College of Biomedical Engineering, Sichuan University, Chengdu, China
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Danala G, Ray B, Desai M, Heidari M, Mirniaharikandehei S, Maryada SKR, Zheng B. Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:459-475. [PMID: 35213340 PMCID: PMC9097354 DOI: 10.3233/xst-221138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Endovascular mechanical thrombectomy (EMT) is an effective method to treat acute ischemic stroke (AIS) patients due to large vessel occlusion (LVO). However, stratifying AIS patients who can and cannot benefit from EMT remains a clinical challenge. OBJECTIVE To develop a new quantitative image marker computed from pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among AIS patients undergoing EMT after diagnosis of LVO. METHODS A retrospective dataset of 31 AIS patients with pre-intervention CTP images is assembled. A computer-aided detection (CAD) scheme is developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and machine learning (ML) models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. Performance of image markers is evaluated using the area under the ROC curve (AUC) and accuracy computed from the confusion matrix. RESULTS The ML model using the neuroimaging features computed from the slopes of the subtracted cumulative blood flow curves between two cerebral hemispheres yields classification performance of AUC = 0.878±0.077 with an overall accuracy of 90.3%. CONCLUSIONS This study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients' prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients.
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Affiliation(s)
- Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
| | | | - Masoom Desai
- Department of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
| | | | | | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
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