Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1-T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study.
Umberto CommitteriRoberta FuscoElio Di BernardoVincenzo AbbateGiovanni SalzanoFabio MaglittoGiovanni Dell'Aversana OrabonaPasquale PiombinoPaola BonavolontàAntonio ArenaFrancesco PerriMaria Grazia MaglioneSergio Venanzio SetolaVincenza GranataGiorgio IaconettaFranco IonnaAntonella PetrilloLuigi CalifanoPublished in: Biology (2022)
). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.
Keyphrases
- lymph node
- early stage
- squamous cell carcinoma
- small cell lung cancer
- lymph node metastasis
- machine learning
- contrast enhanced
- sentinel lymph node
- squamous cell
- neoadjuvant chemotherapy
- computed tomography
- magnetic resonance imaging
- big data
- magnetic resonance
- artificial intelligence
- radiation therapy
- data analysis