Abstract
Silently asymptomatic at an early stage and often painless, requiring only active surveillance, Prostate Cancer (PCa) is traditionally diagnosed by a Digital Rectal Examination (DRE) and a Prostate Specific Antigen (PSA) blood test. A histological examination, searching for pattern irregularities on the prostate glandular tissue, is performed to quantify the aggressiveness of PCa. The assigned Gleason Score (GS), usually combined with Transrectal Ultrasound Guided Biopsy (TRUS), allows the stratification of patients according to their risk group. Intermediate-risk patients may have a favourable (GS = 3 + 4) or unfavourable (GS = 4 + 3) prognosis. This borderline is critical for defining treatments and possible outcomes, while External Beam Radiotherapy (EBRT) is a curative option for localised and locally advanced disease and as a palliative option for metastatic low-volume disease; active surveillance or watchful waiting can also be an option for patients with a favourable prognosis. With radiomics, quantifying phenotypic characteristics in medical imaging is now possible. In the EBRT workflow, there are several imaging modalities, such as Magnetic Ressonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound and Cone Beam Computed Tomography (CBCT). Most radiomic PCa studies focused on MRI and addressed tumour staging, GS, PSA or Biochemical Recurrence (BCR). This study intends to use CBCT radiomics to distinguish between favourable and unfavourable cases, with the potential of evaluating an ongoing treatment. Seven of the most used feature selection methods, combined with 14 different classifiers, were evaluated in a total of 98 pipelines. From those, six stood out with Area Under the Receiver Operating Characteristic (AUROC) values ≥ 0.79. To the best of our knowledge, this is the first work to evaluate a PCa favourable vs. unfavourable prognosis model based on CBCT radiomics.
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