Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer

Ahn, Hee Kyung; Lee, H.; Kim, S.G.; Hyun, Seung Hyup

doi:10.1016/j.crad.2019.02.008

Cited by 61 publications

(55 citation statements)

References 17 publications

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“…Among the various machine-learning techniques, the advantage of RF in being able to predict features non-parametrically even if some features show collinearities with others suggests its suitability for texture analysis. Indeed, Ahn et al reported that an RF classifier provided higher diagnostic performance compared with other machine-learning algorithms, including support vector machine and neural network algorithms, for predicting the prognosis of lung cancer on FDG-PET 14 . The RF classifier technique shows promise for extraction of the most prognostic PET features.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics offers new opportunities for developing a better understanding of oncological processes, enabling personalized therapy 6 , 11 . Some recent radiomics studies have used machine-learning methods such as support vector machines, neural networks, and random forest (RF) classifiers 14 – 16 that can improve the robustness of the statistical analysis 12 . However, few studies have explored the prognostic value of radiomics in pancreatic cancer using FDG-PET/CT with texture analysis 17 – 20 .…”

Section: Introductionmentioning

confidence: 99%

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Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Toyama

Hotta

Motoi

et al. 2020

Sci Rep

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Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using 18F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer. We enrolled 161 patients with pancreatic cancer underwent pretreatment FDG-PET/CT. The area of the primary tumor was semi-automatically contoured with a threshold of 40% of the maximum standardized uptake value, and 42 PET features were extracted. To identify relevant PET parameters for predicting 1-year survival, Gini index was measured using random forest (RF) classifier. Twenty-three patients were censored within 1 year of follow-up, and the remaining 138 patients were used for the analysis. Among the PET parameters, 10 features showed statistical significance for predicting overall survival. Multivariate analysis using Cox HR regression revealed gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) as the only PET parameter showing statistical significance. In RF model, GLZLM GLNU was the most relevant factor for predicting 1-year survival, followed by total lesion glycolysis (TLG). The combination of GLZLM GLNU and TLG stratified patients into three groups according to risk of poor prognosis. Radiomics with machine learning using FDG-PET in patients with pancreatic cancer provided useful prognostic information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Toyama

Hotta

Motoi

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…However, in the study of Park et al, the SVM have shown to have a better predicting performance with an AUC of 0.86 comparing to 0.84 in the RF (40). Whereas it was contradictory in the prognosis prediction superiority between the RF and SVM in a study of lung cancer (54). Actually, the RF has a number of advantages, such as its totally non-parametric property, so that it can be used given the existence of collinearities among features (55).…”

Section: Predicting Pathological Grade Of Meningiomasmentioning

confidence: 93%

The Current State of Radiomics for Meningiomas: Promises and Challenges

Zhang

Russo

et al. 2020

Front. Oncol.

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Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive "wait and see" strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.

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“…The value of radiomics with 18F-fluorodeoxyglucose (FDG)-PET for differential diagnosis and prognosis prediction has been reported for several malignancies, such as lung, breast, esophageal, and cervical cancers, and soft-tissue tumors 12–17 . In addition, some recent radiomics studies have used machine-learning methods like support vector machines, neural networks, and random forest classifiers 18–20 , enabling more robust statistical analysis 8 . However, the efficacy of radiomics using MET-PET for malignant brain tumor has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier

Hotta

Minamimoto

Miwa

2019

Sci Rep

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Differentiating recurrent brain tumor from radiation necrosis is often difficult. This study aims to investigate the efficacy of 11C-methionine (MET)-PET radiomics for distinguishing recurrent brain tumor from radiation necrosis, as compared with conventional tumor-to-normal cortex (T/N) ratio evaluation. We enrolled 41 patients with metastatic brain tumor or glioma treated using radiation therapy who underwent MET-PET. The area with a standardized uptake value > 1.3 times that of the normal brain cortex was contoured. Forty-two PET features were extracted and used in a random forest classifier and the diagnostic performance was evaluated using a 10-fold cross-validation scheme. Gini index was measured to identify relevant PET parameters for classification. The reference standard was surgical histopathological analysis or more than 6 months of follow-up with MRI. Forty-four lesions were used for the analysis. Thirty-three and 11 lesions were confirmed as recurrent brain tumor and radiation necrosis, respectively. Radiomics and T/N ratio evaluation showed sensitivities of 90.1% and 60.6%, and specificities of 93.9% and 72.7% with areas under the curve of 0.98 and 0.73, respectively. Gray level co-occurrence matrix dissimilarity was the most pertinent feature for diagnosis. MET-PET radiomics yielded excellent outcome for differentiating recurrent brain tumor from radiation necrosis, which outperformed T/N ratio evaluation.

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Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer

Cited by 61 publications

References 17 publications

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

The Current State of Radiomics for Meningiomas: Promises and Challenges

11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier

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