Abstract
BackgroundRadiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans.MethodsThe Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT.ResultsRad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous (mathbb {P}^{text {WH}}) which is the current clinical standard, radiomics based focal (mathbb {P}^{text {RF}}), and whole gland with a radiomics based focal boost (mathbb {P}^{text {WF}}). Comparison of mathbb {P}^{text {RF}} against conventional mathbb {P}^{text {WH}} revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. mathbb {P}^{text {WF}} resulted in only a marginal increase in dosage to the OARs compared to mathbb {P}^{text {WH}}. A similar trend was observed in case of EBRT with mathbb {P}^{text {RF}} and mathbb {P}^{text {WF}} compared to mathbb {P}^{text {WH}}.ConclusionsA radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.
Highlights
Radiation therapy (RT) is one of the principal treatment modalities for localized prostate cancer and involves delivering ionizing radiation dose to the prostate, in order to destroy malignant cells
We present a radiomics based decision support framework for radiation treatment planning (Rad-TRaP) for prostate cancer, which combines radiomics based cancer identification and deformable registration methods for Magnetic resonance imaging (MRI)-Computed tomography (CT) fusion
This framework has been used to generate radiomics based focal (PRF), and whole gland with a radiomics based focal boost (PWF) plans using the prediction results of the machine learning classifier
Summary
Radiation therapy (RT) is one of the principal treatment modalities for localized prostate cancer and involves delivering ionizing radiation dose to the prostate, in order to destroy malignant cells. Low-risk PCa patients who are potential candidates for active surveillance but who choose to opt out, usually undergo radical whole gland radiation therapy to ensure no cancer lesions are missed, often resulting in radiation being delivered to the surrounding healthy tissues. This typically results in significant short-term and long-term side effects including incontinence (in 5–20 % patients), sexual dysfunction (30–70 %) and bowel toxicity (5–10 %) [2, 3]. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans
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