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Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer

Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrenc...

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Autores principales: Wang, Yu-De, Huang, Chi-Ping, Yang, You-Rong, Wu, Hsi-Chin, Hsu, Yu-Ju, Yeh, Yi-Chun, Yeh, Pei-Chun, Wu, Kuo-Chen, Kao, Chia-Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648785/
https://www.ncbi.nlm.nih.gov/pubmed/37958276
http://dx.doi.org/10.3390/diagnostics13213380
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author Wang, Yu-De
Huang, Chi-Ping
Yang, You-Rong
Wu, Hsi-Chin
Hsu, Yu-Ju
Yeh, Yi-Chun
Yeh, Pei-Chun
Wu, Kuo-Chen
Kao, Chia-Hung
author_facet Wang, Yu-De
Huang, Chi-Ping
Yang, You-Rong
Wu, Hsi-Chin
Hsu, Yu-Ju
Yeh, Yi-Chun
Yeh, Pei-Chun
Wu, Kuo-Chen
Kao, Chia-Hung
author_sort Wang, Yu-De
collection PubMed
description Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. Methods: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image’s region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier’s effectiveness, the ROC curve area and confusion matrix were employed. Results: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. Conclusions: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.
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spelling pubmed-106487852023-11-03 Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer Wang, Yu-De Huang, Chi-Ping Yang, You-Rong Wu, Hsi-Chin Hsu, Yu-Ju Yeh, Yi-Chun Yeh, Pei-Chun Wu, Kuo-Chen Kao, Chia-Hung Diagnostics (Basel) Article Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. Methods: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image’s region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier’s effectiveness, the ROC curve area and confusion matrix were employed. Results: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. Conclusions: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS. MDPI 2023-11-03 /pmc/articles/PMC10648785/ /pubmed/37958276 http://dx.doi.org/10.3390/diagnostics13213380 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yu-De
Huang, Chi-Ping
Yang, You-Rong
Wu, Hsi-Chin
Hsu, Yu-Ju
Yeh, Yi-Chun
Yeh, Pei-Chun
Wu, Kuo-Chen
Kao, Chia-Hung
Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title_full Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title_fullStr Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title_full_unstemmed Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title_short Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
title_sort machine learning and radiomics of bone scintigraphy: their role in predicting recurrence of localized or locally advanced prostate cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648785/
https://www.ncbi.nlm.nih.gov/pubmed/37958276
http://dx.doi.org/10.3390/diagnostics13213380
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