Predicting Radiotherapy Impact on Late Bladder Toxicity in Prostate Cancer Patients: An Observational Study

Rectum and Bladder Toxicity in Postoperative Prostate Bed Irradiation: Dose-Volume Parameters Analysis

Although prostate cancer treatment is increasingly effective, its toxicities pose a major concern. The aim of our study was to assess the rate of adverse events (AEs) and the prognostic value of dose-volume histogram (DVH) parameters for the occurrence of treatment toxicity in patients treated with post-prostatectomy prostate bed radiotherapy (RT). The AEs were scored according to the CTCAE v.5.0. The rectum and bladder were contoured according to the RTOG Guidelines. The DVH parameters were assessed using data exported from the ECLIPSE treatment-planning system. Genitourinary (GU) and gastrointestinal (GI) toxicity were analysed using consecutive dose thresholds for the percentage of an organ at risk (OAR) receiving a given dose and the QUANTEC dose constraints. A total of 213 patients were included in the final analysis. Acute grade 2 or higher (≥G2) GU AEs occurred in 18.7% and late in 21.3% of patients. Acute ≥G2 GI toxicity occurred in 11.7% and late ≥G2 in 11.2% of the patients. Five patients experienced grade 4 AEs. The most common adverse effects were diarrhoea, proctitis, cystitis, and dysuria. The most significant predictors of acute ≥G2 GI toxicity were rectum V47 and V46 (p < 0.001 and p < 0.001) and rectum wall V46 (p = 0.001), whereas the most significant predictors of late ≥G2 GI AEs were rectum wall V47 and V48 (p = 0.019 and p = 0.021). None of the bladder or bladder wall parameters was significantly associated with the risk of acute toxicity. The minimum doses to bladder wall (p = 0.004) and bladder (p = 0.005) were the most significant predictors of late ≥G2 GU toxicity. Postoperative radiotherapy is associated with a clinically relevant risk of AEs, which is associated with DVH parameters, and remains even in patients who fulfil commonly accepted dose constraints. Considering the lack of survival benefit of postoperative adjuvant RT, our results support delaying treatment through an early salvage approach to avoid or defer toxicity.

Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients

(1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD_2cc) as the most important feature, followed by BD_5cc and the ICRU bladder point. In the case of the lightweight DL model, the F-score and AUROC were 0.90 and 0.91, respectively. (4) The DL models exhibited superior performance in predicting late bladder toxicity compared with the statistical method. Through the interpretation of the model, it further emphasized its potential for improving patient outcomes and minimizing treatment-related complications with a high level of reliability.

Magnetic resonance guided SBRT reirradiation in locally recurrent prostate cancer: a multicentric retrospective analysis

AIMS

Reirradiation of prostate cancer (PC) local recurrences represents an emerging challenge for current radiotherapy. In this context, stereotactic body radiation therapy (SBRT) allows the delivery of high doses, with curative intent. Magnetic Resonance guided Radiation Therapy (MRgRT) has shown promising results in terms of safety, feasibility and efficacy of delivering SBRT thanks to the enhanced soft tissue contrast and the online adaptive workflow. This multicentric retrospective analysis evaluates the feasibility and efficacy of PC reirradiation, using a 0.35 T hybrid MR delivery unit.

METHODS

Patients affected by local recurrences of PC and treated in five institutions between 2019 and 2022 were retrospectively collected. All patients had undergone previous Radiation Therapy (RT) in definitive or adjuvant setting. Re-treatment MRgSBRT was delivered with a total dose ranging from 25 to 40 Gy in 5 fractions. Toxicity according to CTCAE v 5.0 and treatment response were assessed at the end of the treatment and at follow-up.

RESULTS

Eighteen patients were included in this analysis. All patients had previously undergone external beam radiation therapy (EBRT) up to a total dose of 59.36 to 80 Gy. Median cumulative biologically effective dose (BED) of SBRT re-treatment was 213,3 Gy (103,1-560), considering an α/β of 1.5. Complete response was achieved in 4 patients (22.2%). No grade ≥ 2 acute genitourinary (GU) toxicity events were recorded, while gastrointestinal (GI) acute toxicity events occurred in 4 patients (22.2%).

CONCLUSION

The low rates of acute toxicity of this experience encourages considering MRgSBRT a feasibile therapeutic approach for the treatment of clinically relapsed PC. Accurate gating of target volumes, the online adaptive planning workflow and the high definition of MRI treatment images allow delivering high doses to the PTV while efficiently sparing organs at risk (OARs).

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data

Virtual clinical trials (VCTs) can potentially simulate clinical trials on a computer, but their application with a limited number of past clinical cases is challenging due to the biased estimation of the statistical population. In this study, we developed ExMixup, a novel training technique based on machine learning, using iteratively redistributed extrapolated data. Information obtained from 100 patients with prostate cancer and 385 patients with oropharyngeal cancer was used to predict the recurrence after radiotherapy. Model performance was evaluated by developing outcome prediction models based on three types of training methods: training with original data (baseline), interpolation data (Mixup), and interpolation + extrapolation data (ExMixup). Two types of VCTs were conducted to predict the treatment response of patients with distinct characteristics compared to the training data obtained from patient cohorts categorized under risk classification or cancer stage. The prediction models developed with ExMixup yielded concordance indices (95% confidence intervals) of 0.751 (0.719-0.818) and 0.752 (0.734-0.785) for VCTs on the prostate and oropharyngeal cancer datasets, respectively, which significantly outperformed the baseline and Mixup models (P < 0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.

Predictors for late genitourinary toxicity in men receiving radiotherapy for high-risk prostate cancer using planned and accumulated dose

Background and purpose

Significant deviations between bladder dose planned (D_P) and dose accumulated (D_A) have been reported in patients receiving radiotherapy for prostate cancer. This study aimed to construct multivariate analysis (MVA) models to predict the risk of late genitourinary (GU) toxicity with clinical and D_P or D_A as dose-volume (DV) variables.

Materials and methods

Bladder D_A obtained from 150 patients were compared with D_P. MVA models were built from significant clinical and DV variables (p < 0.05) at univariate analysis. Previously developed dose-based-region-of-interest (DB-ROI) metrics using expanded ring structures from the prostate were included. Goodness-of-fit test and calibration plots were generated to determine model performance. Internal validation was accomplished using Bootstrapping.

Results

Intermediate-high D_A (V_{30-65 Gy} and DB-ROI-20-50 mm) for bladder increased compared to D_P. However, at the very high dose region, D_A (D_{0.003 cc}, V_{75 Gy,} and DB-ROI-5-10 mm) were significantly lower. In MVA, single variable models were generated with odds ratio (OR) < 1. DB-ROI-50 mm was predictive of Grade ≥ 1 GU toxicity for D_A and D_P (D_A and D_P; OR: 0.96, p: 0.04) and achieved an area under the receiver operating curve (AUC) of > 0.6. Prostate volume (OR: 0.87, p: 0.01) was significant in predicting Grade 2 GU toxicity with a high AUC of 0.81.

Conclusions

Higher D_A (V_{30-65 Gy}) received by the bladder were not translated to higher late GU toxicity. DB-ROIs demonstrated higher predictive power than standard DV metrics in associating Grade ≥ 1 toxicity. Smaller prostate volumes have a minor protective effect on late Grade 2 GU toxicity.

Predictive modelling for late rectal and urinary toxicities after prostate radiotherapy using planned and delivered dose

Background and purpose

Normal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetric-modulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (D_P) and accumulated dose (D_A) were used.

Material and methods

D_P and D_A obtained from the DVH of 150 prostate cancer patients with pelvic-lymph-nodes irradiation treated using VMAT were used to generate LKB-NTCP parameters using maximum likelihood estimations. Defined GI and GU toxicities were recorded up to 3-years post RT follow-up. Model performance was measured using Hosmer-Lemeshow goodness of fit test and the mean area under the receiver operating characteristics curve (AUC). Bootstrapping method was used for internal validation.

Results

For mild-severe (Grade ≥1) GI toxicity, the model generated similar parameters based on D_A and D_P DVH data (D_A-D₅₀:71.6 Gy vs D_P-D₅₀:73.4; D_A-m:0.17 vs D_P-m:0.19 and D_A/P-n 0.04). The 95% CI for D_A-D₅₀ was narrower and achieved an AUC of >0.6. For moderate-severe (Grade ≥2) GI toxicity, D_A-D₅₀ parameter was higher and had a narrower 95% CI (D_A-D₅₀:77.9 Gy, 95% CI:76.4-79.6 Gy vs D_P-D₅₀:74.6, 95% CI:69.1-85.4 Gy) with good model performance (AUC>0.7). For Grade ≥1 late GU toxicity, D₅₀ and n parameters for D_A and D_P were similar (D_A-D₅₀: 58.8 Gy vs D_P-D₅₀: 59.5 Gy; D_A-n: 0.21 vs D_P-n: 0.19) with a low AUC of<0.6. For Grade ≥2 late GU toxicity, similar NTCP parameters were attained from D_A and D_P DVH data (D_A-D₅₀:81.7 Gy vs D_P-D₅₀:81.9 Gy; D_A-n:0.12 vs D_P-n:0.14) with an acceptable AUCs of >0.6.

Conclusions

The achieved NTCP parameters using modern RT techniques and accounting for organ motion differs from QUANTEC reported parameters. D_A-D₅₀ of 77.9 Gy for GI and D_A/D_P-D₅₀ of 81.7-81.9 Gy for GU demonstrated good predictability in determining the risk of Grade ≥2 toxicities especially for GI derived D₅₀ and are recommended to incorporate as part of the DV planning constraints to guide dose escalation strategies while minimising the risk of toxicity.

Covid-19 and radiotherapy: a systematic review after 2 years of pandemic

Introduction

Following the Covid-19 pandemic spread, changes in clinical practice were necessary to limit the pandemic diffusion. Also, oncological practice has undergone changes with radiotherapy (RT) treatments playing a key role.Although several experiences have been published, the aim of this review is to summarize the current evidence after 2 years of pandemic to provide useful conclusions for clinicians.

Methods

A Pubmed/MEDLINE and Embase systematic review was conducted. The search strategy was "Covid AND Radiotherapy" and only original articles in the English language were considered.

Results

A total of 2.733 papers were obtained using the mentioned search strategy. After the complete selection process, a total of 281 papers were considered eligible for the analysis of the results.

Discussion

RT has played a key role in Covid-19 pandemic as it has proved more resilient than surgery and chemotherapy. The impact of the accelerated use of hypofractionated RT and telemedicine will make these strategies central also in the post-pandemic period.

Dose-volume analysis of planned versus accumulated dose as a predictor for late gastrointestinal toxicity in men receiving radiotherapy for high-risk prostate cancer

•

Interfractional variations in organs at risk were observed in prostate radiotherapy.

•

Rectal accumulated dose was significantly higher at the intermediate-high dose region.

•

Rectal planned dose was significantly higher at the very high dose region.

•

Dose>78.2 Gy to 0.03 cc of rectum was predictive of late Grade 2 toxicity.

•

Patient age>72 years was predictive of late Grade 2 rectal toxicity.

Background and purpose

Significant dose deviations have been reported between planned (D_P) and accumulated (D_A) dose in prostate radiotherapy. This study aimed to develop multivariate analysis (MVA) models associating Grade 1 and 2 gastrointestinal (GI) toxicity with clinical and D_P or D_A dosimetric variables separately.

Materials and methods

Dose volume (DV) metrics were compared between D_A and D_P for 150 high-risk prostate cancer patients. MV models were generated from significant clinical and dosimetric variables (p < 0.05) at univariate level. Dose-based-region of interest (DB-ROI) metrics were included. Model performance was measured, and additional subgroup analysis were performed.

Results

Rectal D_A demonstrated a higher intermediate-high dose (V_{30-65 Gy} and DB-ROI at 15–50 mm) compared to D_P. Conversely, at the very high dose region, rectal D_A (V_{75 Gy} and DB-ROI at 5–10 mm) were significantly lower. In MVA, rectal DB-ROI at 10 mm was predictive for Grade ≥ 1 GI toxicity for D_A and D_P. Age, rectal D_A for D_{0.03 cc}, and rectal D_P for DB-ROI 10 mm were predictors for Grade 2 GI toxicity. Subgroup analysis revealed that patients ≥ 72 years old and a rectal D_A of ≥ 78.2 Gy were highly predictive of Grade 2 GI toxicity.

Conclusions

The dosimetric impact of a higher dose rectal dose in D_A due to volumetric changes was minimal and was not predictive of detrimental clinical toxicity apart from rectal D_{0.03 cc} ≥ 78.2 Gy for Grade 2 GI toxicity. The use of the DB-ROI method can provide equivalent predictive power as the DV method in toxicity prediction.