A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer.
Davide CusumanoGert MeijerJacopo LenkowiczGiuditta ChiloiroLuca BoldriniCarlotta MasciocchiNicola DinapoliRoberto GattaCalogero CasàAndrea DamianiBrunella BarbaroMaria Antonietta GambacortaLuigi AzarioMarco De SpiritoMartijn IntvenVincenzo ValentiniPublished in: La Radiologia medica (2020)
The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.
Keyphrases
- rectal cancer
- locally advanced
- end stage renal disease
- squamous cell carcinoma
- ejection fraction
- chronic kidney disease
- newly diagnosed
- neoadjuvant chemotherapy
- magnetic resonance
- contrast enhanced
- deep learning
- radiation therapy
- peritoneal dialysis
- clinical trial
- high intensity
- electronic health record
- machine learning
- magnetic resonance imaging
- phase ii study
- big data
- study protocol
- artificial intelligence