Radiomics and Molecular Classification in Endometrial Cancer (The ROME Study): A Step Forward to a Simplified Precision Medicine.
Giorgio BoganiValentina ChiappaSalvatore LopezChristian SalvatoreMatteo InterlenghiOttavia D'OriaAndrea GianniniUmberto Leone Roberti MaggioreGiulia ChiarelloSimona PalladinoLudovica Spano' BascioIsabella CastiglioniFrancesco RaspagliesiPublished in: Healthcare (Basel, Switzerland) (2022)
Molecular/genomic profiling is the most accurate method to assess prognosis of endometrial cancer patients. Radiomic profiling allows for the extraction of mineable high-dimensional data from clinical radiological images, thus providing noteworthy information regarding tumor tissues. Interestingly, the adoption of radiomics shows important results for screening, diagnosis and prognosis, across various radiological systems and oncologic specialties. The central hypothesis of the prospective trial is that combining radiomic features with molecular features might allow for the identification of various classes of risks for endometrial cancer, e.g., predicting unfavorable molecular/genomic profiling. The rationale for the proposed research is that once validated, radiomics applied to ultrasonographic images would be an effective, innovative and inexpensive method for tailoring operative and postoperative treatment modalities in endometrial cancer. Patients with newly diagnosed endometrial cancer will have ultrasonographic evaluation and radiomic analysis of the ultrasonographic images. We will correlate radiomic features with molecular/genomic profiling to classify prognosis.
Keyphrases
- endometrial cancer
- deep learning
- single cell
- lymph node metastasis
- convolutional neural network
- clinical trial
- copy number
- single molecule
- prostate cancer
- healthcare
- optical coherence tomography
- patients undergoing
- machine learning
- electronic health record
- computed tomography
- magnetic resonance imaging
- mass spectrometry
- study protocol
- squamous cell carcinoma
- risk assessment
- contrast enhanced
- magnetic resonance
- rectal cancer
- artificial intelligence
- robot assisted
- human health