The benefit of using bladder sub-volume equivalent uniform dose constraints in prostate intensity-modulated radiotherapy planning

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.

A novel knowledge-based prediction model for estimating an initial equivalent uniform dose in semi-auto-planning for cervical cancer

Background

We developed a novel concept, equivalent uniform length (EUL), to describe the relationship between the generalized equivalent uniform dose (EUD) and the geometric anatomy around a tumor target. By correlating EUL with EUD, we established two EUD–EUL knowledge-based (EEKB) prediction models for the bladder and rectum that predict initial EUD values for generating quality treatment plans.

Methods

EUL metrics for the rectum and bladder were extracted and collected from the intensity-modulated radiotherapy therapy (IMRT) plans of 60 patients with cervical cancer. The two EEKB prediction models were built using linear regression to establish the relationships between EUL_r and EUD_r (EUL and EUD of rectum) and EUL_b, and EUD_b (EUL and EUD of bladder), respectively. The EE plans were optimized by incorporating the predicted initial EUD parameters for the rectum and bladder with the conventional pinnacle auto-planning (PAP) initial dose parameters for other organs. The efficiency of the predicted initial EUD values were then evaluated by comparing the consistency and quality of the EE plans, PAP plans (based on default PAP initial parameters), and manual plans (designed manually by different dosimetrists) for a sample of 20 patients.

Results

Linear regression analyses showed a significant correlation between EUL and EUD (R² = 0.79 and 0.69 for EUD_b and EUD_r, respectively). In a sample of 20 patients, the average bladder V40 and V50 derived from the EE plans were significantly lower (V40: 30.00 ± 5.76, V50: 14.36 ± 4.00) than the V40 and V50 values derived from manual plans (V40: 36.03 ± 8.02, V50: 19.02 ± 5.42). Compared with the PAP plans, the EE plans produced significantly lower average V30 and Dmean values for the bladder (V30: 50.55 ± 6.33, Dmean: 31.48 ± 1.97 Gy).

Conclusions

Our EEKB prediction models predicted reasonable initial EUD values for the rectum and bladder based on patient-specific geometric EUL values, thereby improving optimization and planning efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02120-4.

Dosimetric differences between intensity-modulated radiotherapy based on equivalent uniform dose and dose-volume optimization in stage III non-small cell lung cancer

OBJECTIVE

To determine the dosimetric differences between biological and physical functions of equivalent uniform dose (EUD) and dose volume (DV) therapy in patients with phase III non-small cell lung cancer.

METHODS

Four different radiotherapy plans (DV+DV, DV-EUD+DV, EUD+EUD and EUD-DV+EUD) were developed for 15 patients with stage III NSCLC. To study physical function (DV+DV) the target area was optimized by introducing the conditions of biological function optimization, while the organs at risk were optimized by means of physical function (DV-EUD+DV). Biological function optimization (EUD+EUD) was performed for the target area by applying conditions of physical function optimization while biological function optimization (EUD-DV+DV) was conducted for the organs at risk to compare dosimetric parameters among the four groups of treatment plans.

RESULTS

PTV: D_2%, D_98%, D_50%, V_105% and D_max of both the DV-EUD+DV group and EDU-DV+EUD group were the minimum (P<0.05). The minimum and average dose of the EUD+EUD group showed an increasing trend and high-dose area became observable. For homogeneity index (HI), DV-EUD+DV group and EUD-DV+EUD results were compared with the other groups (P<0.05), no significant difference was observed statistically between the DV-EUD+DV group and EUD DV+EUD (P=0.659). With regard to conformability index (CI), the results of the four groups showed no significant difference (P>0.05). For the organs at risk, the mean dose of lung tissue (MLD), V₅, V₁₀, V₂₀, V₃₀, heart V₃₀, V₄₀, and D_mean also revealed no significant difference (P>0.05). For the spinal cord, the D1 % of the EUD+EUD group and EUD-DV+EUD groups were significantly different (P<0.05) than the other groups. While no significant difference (P=0.32) was found between the EUD+EUD and EUD-DV+EUD groups. When comparing the number of machine unions (MU) no significant difference was revealed (P>0.05) among the results of the 4 groups.

CONCLUSION

The methods featuring optimization of physical and biological functions are effective in improving the uniformity of target area to have better outcome of the treatment. Biological function optimization or the combination of biological and physical function optimization is conducive to significantly reduce the required dose for the spinal cord.

Dosimetric predictors and Lyman normal tissue complication probability model of hematological toxicity in cervical cancer patients with treated with pelvic irradiation

PURPOSE

To identify dosimetric parameters associated with acute hematological toxicity (HT) and identify the corresponding normal tissue complication probability (NTCP) model in cervical cancer patients receiving helical tomotherapy (Tomo) or fixed-field intensity-modulated radiation therapy (ff-IMRT) in combination with chemotherapy, that is, concurrent chemoradiotherapy (CCRT) using the Lyman-Kutcher-Burman normal tissue complication probability (LKB-NTCP) model.

METHODS

Data were collected from 232 cervical cancer patients who received Tomo or ff-IMRT from 2015 to 2018. The pelvic bone marrow (PBM) (including the ilium, pubes, ischia, acetabula, proximal femora, and lumbosacral spine) was contoured from the superior boundary (usually the lumbar 5 vertebra) of the planning target volume (PTV) to the proximal end of the femoral head (the lower edge of the ischial tubercle). The parameters of the LKB model predicting ≥grade 2 hematological toxicity (Radiation Therapy Oncology Group [RTOG] grading criteria) (TD50 (1), m, and n) were determined using maximum likelihood analyses. Univariate and multivariate logistic regression analyses were used to identify correlations between dose-volume parameters and the clinical factors of HT.

RESULTS

In total, 212 (91.37%) patients experienced ≥grade 2 hematological toxicity. The fitted normal tissue complication probability model parameters were TD50 (1) = 38.90 Gy (95%CI, [36.94, 40.96]), m = 0.13 (95%CI [0.12, 0.16]), and n = 0.04 (95%CI [0.02, 0.05]). Per the univariate analysis, the NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023), maximal PBM dose (p = 0.01), mean PBM dose (p = 0.021), radiation dose (p = 0.001), and V16-53 (p < 0. 05) were associated with ≥grade 2 HT. The NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023; AUC = 0.87), V16, V17, and V18 ≥ 79.65%, 75.68%, and 72.65%, respectively (p < 0.01, AUC = 0.66∼0.68), V35 and V36 ≥ 30.35% and 28.56%, respectively (p < 0.05; AUC = 0.71), and V47 ≥ 13.43% (p = 0.045; AUC = 0.80) were significant predictors of ≥grade 2 hematological toxicity from the multivariate logistic regression analysis.

CONCLUSIONS

The volume of the PBM of patients treated with concurrent chemoradiotherapy and subjected to both low-dose (V16-18 ) and high-dose (V35,36 and V47 ) irradiation was associated with hematological toxicity, depending on the fractional volumes receiving the variable degree of dosage. The NTCP were stronger predictors of toxicity than V16-18 , V35, 36 , and V47 . Hence, avoiding radiation hot spots on the PBM could reduce the incidence of severe HT.

Investigating spatial fractionation and radiation induced bystander effects: a mathematical modelling approach

Radiation induced bystander effects (RIBEs) have been shown to cause death in cells receiving little or no physical dose. In standard radiotherapy, where uniform fields are delivered and all cells are directly exposed to radiation, this phenomenon can be neglected. However, the role of RIBEs may become more influential when heterogeneous fields are considered. Mathematical modelling can be used to determine how these heterogeneous fields might influence cell survival, but most established techniques account only for the direct effects of radiation. To gain a full appreciation of how non-uniform fields impact cell survival, it is also necessary to consider the indirect effects of radiation. In this work, we utilise a mathematical model that accounts for both the direct effects of radiation on cells and RIBEs. This model is used to investigate how spatially fractionated radiotherapy plans impact cell survivalin vitro. These predictions were compared to survival in normal and cancerous cells following exposure to spatially fractionated plans using a clinical linac. The model is also used to explore how spatially fractionated radiotherapy will impact tumour controlin vivo. Results suggest that spatially fractionated plans are associated with higher equivalent uniform doses than conventional uniform plans at clinically relevant doses. The model predicted only small changes changes in normal tissue complication probability, compared to the larger protection seen clinically. This contradicts a central paradigm of radiotherapy where uniform fields are assumed to maximise cell kill and may be important for future radiotherapy optimisation.

A 2D-3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy

Background

Accurate segmentation of lung lobe on routine computed tomography (CT) images of locally advanced stage lung cancer patients undergoing radiotherapy can help radiation oncologists to implement lobar-level treatment planning, dose assessment and efficacy prediction. We aim to establish a novel 2D–3D hybrid convolutional neural network (CNN) to provide reliable lung lobe auto-segmentation results in the clinical setting.

Methods

We retrospectively collected and evaluated thorax CT scans of 105 locally advanced non-small-cell lung cancer (NSCLC) patients treated at our institution from June 2019 to August 2020. The CT images were acquired with 5 mm slice thickness. Two CNNs were used for lung lobe segmentation, a 3D CNN for extracting 3D contextual information and a 2D CNN for extracting texture information. Contouring quality was evaluated using six quantitative metrics and visual evaluation was performed to assess the clinical acceptability.

Results

For the 35 cases in the test group, Dice Similarity Coefficient (DSC) of all lung lobes contours exceeded 0.75, which met the pass criteria of the segmentation result. Our model achieved high performances with DSC as high as 0.9579, 0.9479, 0.9507, 0.9484, and 0.9003 for left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right lower lobe (RLL), and right middle lobe (RML), respectively. The proposed model resulted in accuracy, sensitivity, and specificity of 99.57, 98.23, 99.65 for LUL; 99.6, 96.14, 99.76 for LLL; 99.67, 96.13, 99.81 for RUL; 99.72, 92.38, 99.83 for RML; 99.58, 96.03, 99.78 for RLL, respectively. Clinician's visual assessment showed that 164/175 lobe contours met the requirements for clinical use, only 11 contours need manual correction.

Conclusions

Our 2D–3D hybrid CNN model achieved accurate automatic segmentation of lung lobes on conventional slice-thickness CT of locally advanced lung cancer patients, and has good clinical practicability.

Split Common Coincidence Point Problem: A Formulation Applicable to (Bio)Physically-Based Inverse Planning Optimization

Inverse planning is a method of radiotherapy treatment planning where the care team begins with the desired dose distribution satisfying prescribed clinical objectives, and then determines the treatment parameters that will achieve it. The variety in symmetry, form, and characteristics of the objective functions describing clinical criteria requires a flexible optimization approach in order to obtain optimized treatment plans. Therefore, we introduce and discuss a nonlinear optimization formulation called the split common coincidence point problem (SCCPP). We show that the SCCPP is a suitable formulation for the inverse planning optimization problem with the flexibility of accommodating several biological and/or physical clinical objectives. Also, we propose an iterative algorithm for approximating the solution of the SCCPP, and using Bregman techniques, we establish that the proposed algorithm converges to a solution of the SCCPP and to an extremum of the inverse planning optimization problem. We end with a note on useful insights on implementing the algorithm in a clinical setting.

Fitting NTCP models to bladder doses and acute urinary symptoms during post-prostatectomy radiotherapy

BACKGROUND

To estimate the radiobiological parameters of three popular normal tissue complication probability (NTCP) models, which describe the dose-response relations of bladder regarding different acute urinary symptoms during post-prostatectomy radiotherapy (RT). To evaluate the goodness-of-fit and the correlation of those models with those symptoms.

METHODS

Ninety-three consecutive patients treated from 2010 to 2015 with post-prostatectomy image-guided intensity modulated radiotherapy (IMRT) were included in this study. Patient-reported urinary symptoms were collected pre-RT and weekly during treatment using the validated Prostate Cancer Symptom Indices (PCSI). The assessed symptoms were flow, dysuria, urgency, incontinence, frequency and nocturia using a Likert scale of 1 to 4 or 5. For this analysis, an increase by ≥2 levels in a symptom at any time during treatment compared to baseline was considered clinically significant. The dose volume histograms of the bladder were calculated. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS) and Logit NTCP models were used to fit the clinical data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC), Akaike information criterion (AIC) and Odds Ratio methods.

RESULTS

For the symptoms of urinary urgency, leakage, frequency and nocturia, the derived LKB model parameters were: 1) D50 = 64.2Gy, m = 0.50, n = 1.0; 2) D50 = 95.0Gy, m = 0.45, n = 0.50; 3) D50 = 83.1Gy, m = 0.56, n = 1.00; and 4) D50 = 85.4Gy, m = 0.60, n = 1.00, respectively. The AUC values for those symptoms were 0.66, 0.58, 0.64 and 0.64, respectively. The differences in AIC between the different models were less than 2 and ranged within 0.1 and 1.3.

CONCLUSIONS

Different dose metrics were correlated with the symptoms of urgency, incontinence, frequency and nocturia. The symptoms of urinary flow and dysuria were poorly associated with dose. The values of the parameters of three NTCP models were determined for bladder regarding four acute urinary symptoms. All the models could fit the clinical data equally well. The NTCP predictions of urgency showed the best correlation with the patient reported outcomes.