MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy

Peeken, Jan C.; Asadpour, Rebecca; Specht, Katja; Chen, Eleanor; Klymenko, Olena; Akinkuoroye, Victor; Hippe, Daniel S.; Spraker, Matthew B.; Schaub, Stephanie K.; Dapper, Hendrik; Knebel, Carolin; Mayr, Nina A.; Gersing, Alexandra S.; Woodruff, Henry C.; Lambin, Philippe; Nyflot, Matthew J.; Combs, Stephanie E.

doi:10.1016/j.radonc.2021.08.023

Cited by 45 publications

(40 citation statements)

References 62 publications

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“…Peeken et al. ( 144 ) performed a post-hoc secondary analysis to determine the final effect in a mixed cohort of two independent institutions based on Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Type III validation requirements. Additionally, clinical trials are placing higher demands on study compliance owing to regulatory restrictions and data protection rules.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, longitudinal studies of multiple imaging from different time points during the treatment offer unique advantages. Peeken et al (144) utilized changes in MRI radiomic features before and after neoadjuvant therapy to predict the pathological complete response in patients with highgrade soft-tissue sarcomas. The results showed that the established "Delta-radiomics" model achieved better performance and reproducibility than a single time point method.…”

Section: Multimodality Analysismentioning

confidence: 99%

“…Zwanenburg and Lock (183) discussed that different TRIPOD analysis types have inherently varying reliabilities, and it is needed to avoid the over-fitting phenomena and build models with external validation. Peeken et al (144) performed a post-hoc secondary analysis to determine the final effect in a mixed cohort of two independent institutions based on Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Type III validation requirements. Additionally, clinical trials are placing higher demands on study compliance owing to regulatory restrictions and data protection rules.…”

Section: Prospective Studies and Clinical Trialsmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Section: Discussionmentioning

confidence: 99%

Section: Multimodality Analysismentioning

confidence: 99%

Section: Prospective Studies and Clinical Trialsmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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“…He analysed the value of delta-radiomics based on T2-weighted sequences in predicting pCR in STS patients before and after neoadjuvant therapy in 65 patients [14]. In a recent study, Peeken et al [15] retrospectively studied 156 patients treated with neoadjuvant chemoradiotherapy and established a delta-radiomics model to predict the e cacy of neoadjuvant therapy for STS, which further con rmed the advantages of radiomics in evaluating the treatment e cacy for STS. However, in previous studies, radiomics was mostly used to evaluate the e cacy in primary lesions of speci c diseases, and the evaluation of radiomics in the lung was mostly based on lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on chest enhanced CT radiomics features: a pilot study

Miao

Jiang

et al. 2022

Preprint

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Objective To investigate the value of enhanced chest CT radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma Methods In a retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma, all received the chemotherapy regimen of epirubicin combined with ifosfamide, and the efficacy was evaluated by the RECIST(1.1). Each patient selected 1 or 2 chemotherapy regimens. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a nonprogressive group (n = 57). The nonprogressive group included a stable group (n = 34) and a partial response group (n = 23). Information of lung metastases was extracted from enhanced CT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better effect in all radiomics models. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance of the model for progression vs. nonprogression. Results In total, 851 enhanced CT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were used to construct a radiomics model. The areas under the curve (AUCs) of the model for predicting the progression of lesions were 0.917 and 0.856 in the training and test groups. Conclusion The model established based on the radiomic features of chest enhanced CT before treatment has certain predictive value for the chemotherapy efficacy of patients with soft tissue sarcoma lung metastases.

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“…Radiomics refers to the technology of analyzing and mining high volumes of quantitative features extracted from medical images and then developing a robust model based on the key information that works to support the clinical decision ultimately ( Limkin et al, 2017 ). It has shown considerable potential in many medical challenges, such as auxiliary diagnosis, classification, and grading of diseases ( Yun et al, 2019 ; Peeken et al, 2021 ). Recently, the application of radiomics combined with ML in the rupture assessment of intracranial aneurysms has shown initial results.…”

Section: Introductionmentioning

confidence: 99%

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

Yang

et al. 2022

Front. Neurosci.

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The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.

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MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy

Cited by 45 publications

References 62 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on chest enhanced CT radiomics features: a pilot study

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

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