Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based 18F-fluorodeoxyglucose positron emission tomography radiomics

Tian, Dong; Shiiya, Haruhiko; Takahashi, Miwako; Terasaki, Yasuhiro; Urushiyama, Hirokazu; Shinozaki‐Ushiku, Aya; Yan, Hang; Sato, Masaaki; Nakajima, Jun

doi:10.1016/j.healun.2022.03.010

Cited by 7 publications

(7 citation statements)

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“…As a novel method to realize personalized risk evaluation, machine learning algorithms can learn the patterns of high-dimensional data of many patients and provide a personalized prediction . Although machine learning is promising in medical practice, few studies have reported its application in LTx . Recently, a study reported a random forests model to predict 1-year survival, but the AUC of this model is 0.62.…”

Section: Discussionmentioning

confidence: 99%

“… 5 Although machine learning is promising in medical practice, few studies have reported its application in LTx. 21 , 22 Recently, a study reported a random forests model to predict 1-year survival, but the AUC of this model is 0.62. To our knowledge, the current study first introduced the RSF algorithm into prognostic research in patients after LTx.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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ImportanceAlthough numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.ObjectiveTo develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.Design, Setting, and ParticipantsThis retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019.Main Outcomes And MeasuresOverall survival in patients after LTx.ResultsA total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P &lt; .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P &lt; .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P &lt; .001), respectively.Conclusions and relevanceIn this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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“…A total of 36 ML models were developed using the six modeling algorithms and six feature selection techniques for predicting LNM. To validate performance, the bootstrap method was applied with 1,000 repetitions as described in previous literature ( 31 ). The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess each model’s overall performance, and a bootstrap resampling methodology was used to assess 95% confidence intervals [CIs].…”

Section: Methodsmentioning

confidence: 99%

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

Zhang²,

Yan³

et al. 2022

Front. Oncol.

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BackgroundFor patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.MethodsPatients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model’s generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied.ResultsOf the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701).ConclusionsML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

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“…Positron emission tomography (PET) imaging has been evaluated and used as a noninvasive tool to assess graft function during an episode of AR, especially in the context of heart and lung transplantation. Tian et al investigated the use of ML-based radiomics analysis of 18F-fluorodeoxyglucose PET images for monitoring allograft rejection in a rat lung transplant model ( 52 ). The researchers found that both the maximum standardized uptake value and radiomics score were correlated with histopathological criteria for rejection.…”

Section: Prognosticationmentioning

confidence: 99%

The transformative potential of artificial intelligence in solid organ transplantation

Al Moussawy,

Lakkis,

Ansari

et al. 2024

Front. Transplant.

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Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

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Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based 18F-fluorodeoxyglucose positron emission tomography radiomics

Cited by 7 publications

References 25 publications

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

The transformative potential of artificial intelligence in solid organ transplantation

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