CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy

Peeken, Jan C.; Bernhofer, Michael; Spraker, Matthew B.; Pfeiffer, Daniela; Devečka, Michal; Ahmed, Tariq; Shouman, M.; Ott, Armin; Nüsslin, Fridtjof; Mayr, Nina A.; Rost, Burkhard; Nyflot, Matthew J.; Combs, Stephanie E.

doi:10.1016/j.radonc.2019.01.004

Cited by 66 publications

(53 citation statements)

References 33 publications

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“…Although patient numbers were substantially low and no external validation cohorts were used, the studies demonstrated the principal potential to correlate quantitative radiomic features to tumor grading. A further study could demonstrate the differentiation of G2 and G3 STS using computer tomography-based radiomics albeit with a low predictive performance with an AUC of 0.65 [18]. Although radiomics features seem to provide information regarding the differentiation of G2 or G3 STS, there is currently no clinical benefit for such a differentiation.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, first studies have indicated the potential of radiomics to predict tumor grading in multiple cancers such as neuroendocrine pancreatic tumors or gliomas [13,14]. For STS, two previous studies described the potential of radiomics to predict overall survival, distant disease progression, and response to neoadjuvant chemotherapy [[15], [16], [17], [18]].…”

Section: Introductionmentioning

confidence: 99%

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Tumor grading of soft tissue sarcomas using MRI-based radiomics

et al. 2019

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a b s t r a c tBackground: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. Methods: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. Findings: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. Interpretation: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tumor grading of soft tissue sarcomas using MRI-based radiomics

et al. 2019

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“…Segal et al [13] demonstrated that features in radiological images can be used to reconstruct the majority of the tumor genetic profile. Radiomics data successfully predicted overall survival [14,15], metastases development [16,17] or histological properties [18,19] and may be used as a decision support system in clinical practice. Radiomics approaches to determine the HPV status achieved areas under the receiver operating characteristic curve (AUC) of about 70% to 80%, when tested on external data sets [20,21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Lang

Peeken

Combs

et al. 2021

Cancers

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Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

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“…Extracted features quantify intensity distributions, shape properties, and texture parameters such as "heterogeneity" in previously defined volumes of interest (VOI) [16,17]. After incorporation into prediction models, such features can be used effectively to predict prognosis, histological properties, and molecular aberrations [18][19][20][21]. In PC, previous studies could demonstrate successful prediction of Gleason score and survival using radiomic models [22,23].…”

Section: Introductionmentioning

confidence: 99%

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

Peeken

Shouman

Kroenke

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)–positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). Methods Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. Results Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node–specific decision curve analysis, there was a clinical net benefit above LN short diameter. Conclusion The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features. Electronic supplementary material The online version of this article (10.1007/s00259-020-04864-1) contains supplementary material, which is available to authorized users.

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CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy

Cited by 66 publications

References 33 publications

Tumor grading of soft tissue sarcomas using MRI-based radiomics

Tumor grading of soft tissue sarcomas using MRI-based radiomics

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

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