Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization
Abstract Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patie...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27007-y |
| _version_ | 1828083375259254784 |
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| author | Weiwei Zong Eric Carver Simeng Zhu Eric Schaff Daniel Chapman Joon Lee Hassan Bagher-Ebadian Benjamin Movsas Winston Wen Tarik Alafif Xiangyun Zong |
| author_facet | Weiwei Zong Eric Carver Simeng Zhu Eric Schaff Daniel Chapman Joon Lee Hassan Bagher-Ebadian Benjamin Movsas Winston Wen Tarik Alafif Xiangyun Zong |
| author_sort | Weiwei Zong |
| collection | DOAJ |
| description | Abstract Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians. |
| first_indexed | 2024-04-11T04:08:00Z |
| format | Article |
| id | doaj.art-f436834c607d4632a41009330e5b1025 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T04:08:00Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f436834c607d4632a41009330e5b10252023-01-01T12:19:07ZengNature PortfolioScientific Reports2045-23222022-12-011211810.1038/s41598-022-27007-yProstate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilizationWeiwei Zong0Eric Carver1Simeng Zhu2Eric Schaff3Daniel Chapman4Joon Lee5Hassan Bagher-Ebadian6Benjamin Movsas7Winston Wen8Tarik Alafif9Xiangyun Zong10WeCare.WeTeachHenry Ford Health SystemHenry Ford Health SystemHenry Ford Health SystemHenry Ford Health SystemTrinity HealthHenry Ford Health SystemHenry Ford Health SystemSJTU-Ruijing-UIH Institute for Medical Imaging TechnologyUmm Al-Qura UniversityShanghai JiaoTong University Affiliated Sixth People’s HospitalAbstract Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.https://doi.org/10.1038/s41598-022-27007-y |
| spellingShingle | Weiwei Zong Eric Carver Simeng Zhu Eric Schaff Daniel Chapman Joon Lee Hassan Bagher-Ebadian Benjamin Movsas Winston Wen Tarik Alafif Xiangyun Zong Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization Scientific Reports |
| title | Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization |
| title_full | Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization |
| title_fullStr | Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization |
| title_full_unstemmed | Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization |
| title_short | Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization |
| title_sort | prostate cancer malignancy detection and localization from mpmri using auto deep learning as one step closer to clinical utilization |
| url | https://doi.org/10.1038/s41598-022-27007-y |
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