Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal Methods

Abstract

Objective Soft-tissue sarcomas (STSs) of the extremities are a group of malignancies arising from the mesenchymal cells that may develop distant metastases or local recurrence. In this article, we propose a novel methodology aimed to predict metastases and recurrence risk in patients with these malignancies by evaluating magnetic resonance radiomic features that will be formally verified through formal logic models. Materials and Methods This is a retrospective study based on a public dataset evaluating MRI scans T2-weighted fat-saturated or short tau inversion recovery and patients having “metastases/local recurrence” (group B) or “no metastases/no local recurrence” (group A) as clinical outcomes. Once radiomic features are extracted, they are included in formal models, on which is automatically verified the logic property written by a radiologist and his computer scientists coworkers. Results Evaluating the Formal Methods efficacy in predicting distant metastases/local recurrence in STSs (group A vs group B), our methodology showed a sensitivity and specificity of 0.81 and 0.67, respectively; this suggests that radiomics and formal verification may be useful in predicting future metastases or local recurrence development in soft tissue sarcoma. Discussion Authors discussed about the literature to consider Formal Methods as a valid alternative to other Artificial Intelligence techniques. Conclusions An innovative and noninvasive rigourous methodology can be significant in predicting local recurrence and metastases development in STSs. Future works can be the assessment on multicentric studies to extract objective disease information, enriching the connection between the radiomic quantitative analysis and the radiological clinical evidences.

The impact of radiomics in the management of soft tissue sarcoma

“…Liu et al [ 33 ] evaluated the accuracy of two deep learning-radiomic nomogram models, in conjunction with clinical parameters, for predicting local recurrence in patients with STSs who underwent surgical resection. Lastly, one recent study presented a methodology employing MRI radiomic features for the prediction of metastasis and recurrence risk in patients with extremity STSs using formal logic models [ 34 ].…”

“…Two datasets were specifically analysed for STSs, the first (LIPO) to classify well-differentiated liposarcoma or lipoma, and the second (Desmoid) to classify desmoid-type fibromatosis or extremity STSs The best radiomics approach achieved an AUC of 0.86 for the LIPO dataset and 0.844 for Desmoid dataset. The best deep convolutional neural networks approach achieved an AUC of 0.982 for the LIPO dataset and 0.961 for Desmoid dataset 10.1002/jmri.28331 Lee et al (Republic of Korea) [ 30 ] March 2023 Risk stratification Retrospective MRI 72 1132 To investigate the effectiveness of a radiomics model using T2-weighted Dixon sequence in differentiating the degree of STSs margin infiltration The radiomics model constructed with radiomic volume and shape and other T2 features showed the highest AUC (0.821) both for the models generated by LASSO + RF and LASSO + SMOTE + RF algorithms 10.3390/cancers15072150 Foreman et al (Germany) [ 48 ] 2023 Apr Diagnosis and Radiogenomics Retrospective MRI 307 312 To build and validate radiogenomic models aimed at predicting the MDM2 gene amplification status and differentiating between atypical lipomatous tumours and lipomas using preoperative MRI scans The LASSO classifier, utilising radiomic features extracted from all imaging sequences, exhibited excellent performance, achieving an AUC of 0.88 in the testing dataset 10.1093/jamiaopen/ooad025 Casale et al (Belgium) [ 34 ] 2023 Apr Risk stratification Retrospective MRI 47 102 To propose a methodology that utilised formal logic models to predict the risk of metastases and recurrence in patients with extremity STSs by analysing MRI radiomic features The sensitivity and specificity of the methodology were found to be 0.81 and 0.67, respectively 10.1177/02841851231179933 Zhu et al (China) [ 43 ] 2023 Jun Radiogenomics Ret...…”

The Impact of Edema on MRI Radiomics for the Prediction of Lung Metastasis in Soft Tissue Sarcoma

“…Escobar et al [36] developed a model to predict the onset of lung metastases using MRI sequences, and achieved an AUC of 0.840 using BootstrapOutOfBag. In [37], a methodology utilizing formal logic and radiomics models to predict the risk of metastasis or recurrence yielded an accuracy of 0.74. However, unlike our study, the first three works employed segmentations that included only the GTV, while the fourth study used segmentations that included both the GTV and edema together.…”

AI applications in musculoskeletal imaging: a narrative review

“…Radiomics refers to the extraction and analysis of quantitative features from medical images, known as radiomic features, which may be used to support decision-making algorithms [71]. In musculoskeletal oncology, most AI-based radiomic studies focused on prediction of diagnosis-such as benign versus malignant tumor discrimination [72] or tumor grading [73]-and outcome-such as therapy response [27,74], recurrence [28,75], and survival [29]. In particular, several diagnosis-related studies dealt with benign versus malignant (or intermediate, like atypical cartilaginous or lipomatous tumors) discrimination and grading in skeletal cartilaginous tumors [23][24][25]76], lipomatous soft-tissue tumors [77,78] and soft-tissue sarcomas [26].…”