AUTHOR=Voigtländer Hendrik , Kauczor Hans-Ulrich , Sedaghat Sam 

TITLE=Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1531781

DOI=10.3389/fonc.2025.1531781

ISSN=2234-943X

ABSTRACT=PurposeThis review assesses the diagnostic performance of MRI-based convolutional neural networks for identifying and grading soft tissue sarcomas, evaluating therapy responses, and assessing the risk for metastases and recurrences.MethodsElectronic databases, specifically PubMed/MEDLINE and Google Scholar, were diligently scoured for studies that delved into the intersection of convolutional neural networks, soft tissue sarcomas, and MRI. Three topics were included: 1) differentiating and grading soft tissue sarcomas, 2) assessing therapy response, and 3) predicting metastases and recurrences.ResultsThis review included 12 articles. Seven articles investigated the differentiation and grading of soft tissue sarcomas. Sensitivity for that issue ranged from 0.85 to 0.95, specificity from 0,33 to 1, and the area under the curve (AUC) from 0.74 to 0.96. Three articles investigated therapy responses, and two discussed metastasis and recurrence prediction. Only one article out of the five articles above presented accurate diagnostic values. That article examined the prediction of lung metastases and demonstrated a sensitivity of 0.47, a specificity of 0.97, and an AUC of 0.83.ConclusionAI applications using CNNs demonstrated robust capabilities in differentiating and grading soft tissue sarcomas using MRI. However, studies on therapy response and prediction of metastases and recurrences are still lacking.