# Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/radiomics-based-machine-learning-model-for-predicting-overall-and-progression/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Michela Destito,Aldo Marzullo,Riccardo Leone,Paolo Zaffino,Sara Steffanoni,Federico Erbella,Francesco Calimeri,Nicoletta Anzalone,Elena De Momi,Andrés J.M. Ferreri,Teresa Calimeri,Maria Francesca Spadea |
| Citations | 24 |
| DOI | 10.3390/bioengineering10030285 |
| Fields | Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2306-5354/10/3/285/pdf?version=1677057416 |
| OpenAlex ID | https://openalex.org/W4321494843 |
| PMID | 36978676 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Aldo Marzullo](https://scholariq.org/researchers/aldo-marzullo/)

## Paper journal

- [Bioengineering](https://scholariq.org/journals/bioengineering/)

## Paper primary topic

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

## Paper topics

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [CNS Lymphoma Diagnosis and Treatment](https://scholariq.org/topics/cns-lymphoma-diagnosis-and-treatment/)
- [MRI in cancer diagnosis](https://scholariq.org/topics/mri-in-cancer-diagnosis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.