Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy

Assessment of rectal toxicities after radiation therapy for localized prostate cancer: experience of the Akanda Cancer Institute in Gabon

Background

The purpose was to evaluate the incidence of acute and late rectal toxicities and their correlation with the clinical and dosimetric parameters of patients who underwent curative radiotherapy for localized prostate cancer at the Akanda Cancer Institute, Gabon.

Materials and methods

Between 2013 and 2021, a cohort of 46 patients with clinically localized stage cT1c-T4 prostate cancer was treated with three-dimensional conformal radiation therapy (3D-CRT) at the national cancer institute with doses ranging from 66 to 80 Gy. Post-radiation gastrointestinal (GI) toxicities were classified and graded according to the Common Terminology Criteria for Adverse Events CTCAE v4.0.

Results

In our study, 17.4% (8/46) developed acute GI. Grades 1 and 3 acute GI complications were seen in 13.0% (6/46) and 4.3% (2/46), respectively. No patient developed acute grade 2 or grade higher than 3 complications. Late GI side effects were limited. The median time to the development of late GI Grade ≥ 1 toxicities was 12 months (range: 9-19 months). 10.9% (5/46) had experience late GI. Among them, grade 1 and 2 were seen in 6.5% (3/46), and 4.3% (2/46), respectively. There was no grade 3 or higher complications. Statistically, we did not find any correlation between the presence of rectal toxicity and clinical factors or the presence of comorbidity. On the dosimetric level, the Mann-Whitney statistical test found a correlation between the presence of late GI toxicity and rectal volume irradiated at the prescribed dose (p = 0.02).

Conclusion

Despite the high radiation doses involved, our results showed an acceptable complication rate.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization

Background and Purpose

The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation.

Materials and Methods

Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan).

Results

The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage.

Conclusions

Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy

Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution. They allow to identify anatomical subregions of one or several organs in which the irradiation dose is associated with a given toxicity. These methods rely on an accurate anatomical alignment, usually obtained by means of a non-rigid registration. Once the different anatomies are spatially normalised, correlations between the three-dimensional dose and a given toxicity can be explored voxel-wise. Parametric or non-parametric statistical tests can be performed on every voxel to identify the voxels in which the dose is significantly different between patients presenting or not toxicity. Several anatomical subregions associated with genitourinary, gastrointestinal, cardiac, pulmonary or haematological toxicity have already been identified in the literature for prostate, head and neck or thorax irradiation. Voxel-wise analysis appears therefore first particularly interesting to increase toxicity prediction capability by identifying specific subregions in the organs at risk whose irradiation is highly predictive of specific toxicity. The second interest is potentially to decrease the radio-induced toxicity by limiting the dose in the predictive subregions, while not decreasing the dose in the target volume. Limitations of the approach have been pointed out.

Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning

OBJECTIVES

Radiation therapy is a main method for female pelvic malignancies, which can cause some adverse reactions, such as radiation proctitis (RP). The incidence of RP is highly positively correlated with radiation dose. There is an urgent need for a scientific method to accurately predict the occurrence of RP to help doctors make clinical decisions. In this study, based on the clinical data of female pelvic tumor patients and dosimetric parameters of radiotherapy, the random forest method was used to screen the hub features related to the occurrence of RP, and then a machine learning algorithm was used to construct a risk prediction model for the occurrence of RP, in order to provide technical support and theoretical basis for the prediction and prevention of RP.

METHODS

A total of 100 female patients with pelvic tumors, who received static three-dimensional conformal intensity-modulated radiation therapy in the Department of Radiation Oncology of the Affiliated Hospital of Xiangnan University from January 2019 to December 2020, were retrospectively collected, and their clinically relevant data and radiotherapy planning system data were collected. During radiotherapy and 18 months after radiotherapy, 35 cases developed RP (RP group), and the remaining 65 cases had no RP (non-RP group). The clinical and dosimetric characteristics of patients were ranked by the importance of random forest algorithm, and the independent prognostic characteristics associated with the occurrence of RP were selected for machine learning modeling. A total of 6 machine learning algorithms including support vector machines, random forests, logistic regression, lightweight gradient boosting machines, Gaussian naïve Bayes, and adaptive enhancement were used to build models. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Finally, the random forest model was determined as the prediction model, and the calibration curve and decision curve of the prediction model were drawn to evaluate the accuracy and clinical benefit of the model.

RESULTS

The parameters for random forest prediction model in the training set were as follow: AUC, 1.000, accuracy, 0.988, sensitivity, 1.000, specificity, 1.000, positive predictive value, 1.000, negative predictive value, 0.981, and F1 score, 1.000. In validation set, AUC was 0.713, accuracy was 0.640, sensitivity was 0.618, specificity was 0.822, positive predictive value was 0.500, negative predictive value was 0.656, and F1 score was 0.440. Random forest showed high predictive performance. Moreover, the Brief of the calibration curve for the prediction model was 0.178, the prediction accuracy was high, and the decision curve showed that the prediction model could benefit clinically.

CONCLUSIONS

Based on the clinical and dosimetric parameters for the female pelvic tumor patients, the prediction model of radiation proctitis constructed by random forest algorithm has high predictive ability and strong clinical usability.

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.

A Ten-year-long Update on Radiation Proctitis Among Prostate Cancer Patients Treated With Curative External Beam Radiotherapy

This comprehensive synopsis summarizes the most relevant information obtained from a systematic analysis of studies of the last decade on radiation proctitis, one of the most feared radioinduced side effects among prostate cancer patients treated with curative external beam radiotherapy. The present review provides a useful support to radiation oncologists for limiting the onset or improving the treatment of radiation proctitis. This work shows that the past decade was a harbinger of significant new evidence in technological advances and technical tricks to avoid radiation proctitis, in addition to dosimetric perspectives and goals, understanding of pathogenesis, diagnostic work-up and treatment. We believe that a well-rounded knowledge of such an issue is fundamental for its appropriate management.