Dataset Information

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.

SUBMITTER: Casale R

PROVIDER: S-EPMC10097456 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Casale Roberto R Varriano Giulia G Santone Antonella A Messina Carmelo C Casale Chiara C Gitto Salvatore S Sconfienza Luca Maria LM Bali Maria Antonietta MA Brunese Luca L

JAMIA open 20230412 2

<h4>Objective</h4>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.<h4>Materials and methods</h4>This is a retrospective study based on a pu ...[more]

PMID: 37063407

Similar Datasets

Project description:BackgroundPrognostic models for patients with soft tissue sarcoma (STS) of the extremities have been developed from large multi-institutional datasets with mixed results. We aimed to develop predictive nomograms for sarcoma-specific survival (SSS) and, for the first time, long-term local recurrence (LR) and distant recurrence (DR) in patients with STS of the extremities treated at our institution.Patients and methodsData from patients treated at Humanitas Cancer Center from 1997 to 2015 were analyzed. Variable selection was based on the clinical knowledge and multivariable regression splines algorithm. Perioperative treatments were always included in the model. Prognostic models were developed using Cox proportional hazards model, and model estimates were plotted in nomograms predicting SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. Model performance was estimated internally via bootstrapping, in terms of optimism-corrected discrimination (Harrell C-index) and calibration (calibration plots).ResultsData on 517 patients were analyzed. At 5 and 10 years, SSS was 68.1% [95% confidence interval (CI), 63.8-72.1] and 55.6% (50.5-60.3), respectively. LR was 79.1% (95% CI, 75.3-82.4), 71.1% (95% CI, 66.7-75.1), and 66.0% (95% CI, 60.7-70.7) at 2, 5, and 10 years, respectively, whereas DR was 65.9% (95% CI, 61.6-69.9), 57.5% (95% CI, 53.0-61.8), and 52.1% (95% CI, 47.1-56.8) at 2, 5, and 10 years, respectively. SSS nomogram included age, gender, margins, tumor size, grading, and histotype. LR and DR nomograms incorporated mostly the same variables, except for age for DR; LR nomogram did not include gender but included anatomic site. The optimism-corrected C-indexes were 0.73 and 0.72 for SSS at 5 and 10 years, respectively; 0.65, 0.64, and 0.64 for LR at 2, 5, and 10 years, respectively; and 0.68 for DR at 2, 5, and 10 years. Predicted probabilities were close to the observed ones for all outcomes.ConclusionsWe developed and validated three nomograms for STS of the extremities predicting the probability of SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. By accounting for the perioperative treatment, these models allow prediction for future patients who had no perioperative treatment, thus being useful in the clinical decision-making process.

Dataset Information

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

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets