Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Huang, Brian; Sollee, John; Luo, Yongheng; Reddy, Ashwin; Zhong, Zhusi; Wu, Jing; Mammarappallil, J.; Healey, Terrance T.; Cheng, Gang; Korogodsky, Dana; Zhang, Paul; Feng, Xue; Li, Jie; Li, Yang; Jiao, Zhicheng; Bai, Harrison X.

doi:10.1016/j.ebiom.2022.104127

Cited by 38 publications

(21 citation statements)

References 42 publications

(59 reference statements)

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“…Some studies have also shown that combining CT with PET or SPECT can result in superior diagnostic or predictive accuracy. [31][32][33] How-ever, multimodal imaging faces some challenges like image alignment issues and the overfitting problems due to the increased amount of data. 34 In our study, SPECT and CT images were acquired simultaneously, which resulted in more accurate spatial alignment and less VOI error.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Wang,

Chen,

Qiu

et al. 2023

Medical Physics

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BackgroundBone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination.PurposeMachine learning analysis of [99mTc]‐methylene diphosphate (99mTc‐MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer.MethodsOne hundred forty‐one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK‐SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics‐clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test.ResultsThe optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively.ConclusionsThe machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics‐clinical integrated model demonstrated the best performance.

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Section: Discussionmentioning

confidence: 99%

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Wang,

Chen,

Qiu

et al. 2023

Medical Physics

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“…Accordingly, ML prediction models have been used for prognosticating outcomes of survival, recurrence, and morbidity in cancers of the breast, 23,24 colorectal cancer, 57,58 and lung cancer. [59][60][61] Regressionbased prediction models include LR, EN, and LL. Due to their normalization penalties, LL and EN prefer to shrink the estimated coefficients as much as possible given the same level of estimation error, thus minimizing overfitting.…”

Section: Model Developmentmentioning

confidence: 99%

Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction

Chen,

Chawla,

Mousa-Doust

et al. 2024

Plastic and Reconstructive Surgery - Global Open

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Background: Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods: In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results: In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65–0.80]; RF 0.77 AUC (95% CI, 0.74–0.81); EN 0.77 AUC (95% CI, 0.73–0.81); logistic lasso 0.76 AUC (95% CI, 0.72–0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730–0.858)]. Conclusion: A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.

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“…Although these studies have achieved varied success, they have focused on predicting the binary results at specific time points. Huang et al extend features extracted with the CNN to the prediction of survival as a continuous outcome by incorporating the random survival forest model, which outperformed corresponding CT-only and PET-only models (77). We summarized the main findings on treatment outcome and survival in Table 4.…”

Section: Treatment Outcome and Survivalmentioning

confidence: 99%

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Yang

et al. 2023

Front. Oncol.

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ObjectivesLung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).Materials and methodsA comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.ResultsClassification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.ConclusionAI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

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Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Cited by 38 publications

References 42 publications

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

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