Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study

Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

“…For instance, tumor characterization in terms of molecular aberrations ("radiogenomics") or histological properties could be a potential outcome target. Multiple authors demonstrated noninvasive prediction of the important prognostic factor, "tumor grading" [58,59]. In an ongoing work, we could demonstrate promising results differentiating benign lipomas from atypical lipomatous tumors based on the murine double minutes (MDM2) gene amplification status.…”

Section: Discussionmentioning

confidence: 86%

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Peeken

Neumann

Asadpour

et al. 2021

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

“…As previously mentioned, other authors have evaluated tumor grading prediction using MRI-based radiomics [38][39][40][41][42]. However, only one study validated their models in an external testing cohort [40]. In this study, Yan et al used a training cohort of 109 patients to develop radiomic models based on T2FS and T1-weighted MRI sequences (without contrastenhancement).…”

Section: Discussionmentioning

confidence: 99%

“…Overall survival (OS) was calculated from the initial pathologic diagnosis to the time point of death or the time point of censoring. Data reporting follows the STARD recommendations (Table S1: STARD checklist) [40].…”

Section: Patientsmentioning

confidence: 99%

“…Wang et al developed radiomic models to differentiate malignant and benign soft-tissue lesions [36]. Further research studies evaluated the differentiation of high-grade from low-grade STS based on MRI and CT imaging scans using radiomic analysis [37][38][39][40][41][42]. No study has yet analyzed the possibility of DL-based tumor grading prediction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Navarro

Dapper

Asadpour

et al. 2021

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.