Background and purpose

Many dose-limiting normal tissues in radiotherapy (RT) display considerable internal motion between fractions over a course of treatment, potentially reducing the appropriateness of using planned dose distributions to predict morbidity. Accounting explicitly for rectal motion could improve the predictive power of modelling rectal morbidity. To test this, we simulated the effect of motion in two cohorts.

Materials and methods

The included patients (232 and 159 cases) received RT for prostate cancer to 70 and 74 Gy. Motion-inclusive dose distributions were introduced as simulations of random or systematic motion to the planned dose distributions. Six rectal morbidity endpoints were analysed. A probit model using the QUANTEC recommended parameters was also applied to the cohorts.

Results

The differences in associations using the planned over the motion-inclusive dose distributions were modest. Statistically significant associations were obtained with four of the endpoints, mainly at high doses (55-70 Gy), using both the planned and the motion-inclusive dose distributions, primarily when simulating random motion. The strongest associations were observed for GI toxicity and rectal bleeding (Rs=0.12-0.21; Rs=0.11-0.20). Applying the probit model, significant associations were found for tenesmus and rectal bleeding (Rs=0.13, p=0.02).

Conclusion

Equally strong associations with rectal morbidity were observed at high doses (>55 Gy), for the planned and the simulated dose distributions including in particular random rectal motion. Future studies should explore patient-specific descriptions of rectal motion to achieve improved predictive power.

Purpose

To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.

Methods

A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.

Results

The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.

Conclusions

We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

Purpose: To demonstrate that a mathematical model can be used to quantitatively understand tumor cellular dynamics during a course of radiotherapy and to predict the likelihood of local control as a function of dose and treatment fractions.Experimental Design: We model outcomes for early-stage, localized non-small cell lung cancer (NSCLC), by fitting a mechanistic, cellular dynamics-based tumor control probability that assumes a constant local supply of oxygen and glucose. In addition to standard radiobiological effects such as repair of sub-lethal damage and the impact of hypoxia, we also accounted for proliferation as well as radiosensitivity variability within the cell cycle. We applied the model to 36 published and two unpublished early-stage patient cohorts, totaling 2,701 patients.Results: Precise likelihood best-fit values were derived for the radiobiological parameters: α [0.305 Gy^-1; 95% confidence interval (CI), 0.120-0.365], the α/β ratio (2.80 Gy; 95% CI, 0.40-4.40), and the oxygen enhancement ratio (OER) value for intermediately hypoxic cells receiving glucose but not oxygen (1.70; 95% CI, 1.55-2.25). All fractionation groups are well fitted by a single dose-response curve with a high χ² P value, indicating consistency with the fitted model. The analysis was further validated with an additional 23 patient cohorts (n = 1,628). The model indicates that hypofractionation regimens overcome hypoxia (and cell-cycle radiosensitivity variations) by the sheer impact of high doses per fraction, whereas lower dose-per-fraction regimens allow for reoxygenation and corresponding sensitization, but lose effectiveness for prolonged treatments due to proliferation.Conclusions: This proposed mechanistic tumor-response model can accurately predict overtreatment or undertreatment for various treatment regimens. Clin Cancer Res; 23(18); 5469-79. ©2017 AACR.

Background and purpose

The risk of genitourinary (GU) toxicity is dose-limiting in radiotherapy (RT) for prostate cancer. This study investigated whether motion-inclusive spatial dose/volume metrics explain the GU toxicity manifesting after high-precision RT for prostate cancer.

Material and methods

A matched case-control was performed within a cohort of 258 prostate cancer patients treated with daily cone-beam CT (CBCT)-guided RT (prescription doses of 77.4-81.0 Gy). Twenty-seven patients (10.5%) presented late RTOG GU ≥ Grade 2 toxicity and those without symptoms of toxicity prior treatment (N = 7) were selected as cases. Each case was matched with three controls based on pre-treatment GU symptoms, age, Gleason score, follow-up time, and hormone therapy. Thirteen CBCTs per patient were rigidly registered to the planning CT using the recorded treatment shifts, and the bladder was manually contoured on each CBCT. Planned and actually delivered dose/volume metrics (the latter averaged across the CBCTs) were extracted from the bladder and its subsectors, and compared between cases and controls (two-way ANOVA test).

Results

There were no significant differences between planned and delivered dose/volume metrics; also, there were no significant differences between cases and controls at any dose level, neither for planned nor delivered doses. The cases tended to have larger bladder volumes during treatment than controls (221 ± 71 cm³ vs 166 ± 73 cm³; p = 0.09).

Conclusions

High-precision RT for prostate cancer eliminates differences between planned and delivered dose distributions. Neither planned nor delivered bladder dose/volume metrics were associated to the remaining low risk of developing GU toxicity after high-precision radiotherapy for prostate cancer.

Background

To investigate the dose-volume factors in mastication muscles that are implicated as possible causes of trismus in patients following treatment with intensity-modulated radiotherapy (IMRT) and concurrent chemotherapy for head and neck cancers.

Material and methods

All evaluable patients treated at our institution between January 2004 and April 2009 with chemotherapy and IMRT for squamous cell cancers of the oropharynx, nasopharynx, hypopharynx or larynx were included in this analysis (N = 421). Trismus was assessed using CTCAE 4.0. Bi-lateral masseter, temporalis, lateral pterygoid and medial pterygoid muscles were delineated on axial computed tomography (CT) treatment planning images, and dose-volume parameters were extracted to investigate univariate and multimetric correlations.

Results

Forty-six patients (10.9%) were observed to have chronic trismus of grade 1 or greater. From analysis of baseline patient characteristics, toxicity correlated with primary site and patient age. From dose-volume analysis, the steepest dose thresholds and highest correlations were seen for mean dose to ipsilateral masseter (Spearman's rank correlation coefficient Rs = 0.25) and medial pterygoid (Rs = 0.23) muscles. Lyman-Kutcher-Burman modeling showed highest correlations for the same muscles. The best correlation for multimetric logistic regression modeling was with V68Gy to the ipsilateral medial pterygoid (Rs = 0.29).

Conclusion

Chemoradiation-induced trismus remains a problem particularly for patients with oropharyngeal carcinoma. Strong dose-volume correlations support the hypothesis that limiting dose to the ipsilateral masseter muscle and, in particular, the medial pterygoid muscle may reduce the likelihood of trismus.

Background

The aims of this study were to investigate temporal patterns and potential risk factors for severe hyposalivation (xerostomia) after intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC), and to test the two QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic) guidelines.

Patients and methods

Sixty-three patients treated at the Memorial Sloan Kettering Cancer Center between 2006 and 2015, who had a minimum of three stimulated whole mouth saliva flow measurements (WMSFM) at a median follow-up time of 11 (range: 3-24) months were included. Xerostomia was defined as WMSFM ≤25% compared to relative pre-radiotherapy. Patients were stratified into three follow-up groups: 1: <6 months; 2: 6-11 months; and 3: 12-24 months. Potential risk factors were investigated (Mann-Whitney U test), and relative risks (RRs) assessed for the two QUANTEC guidelines.

Results

The incidence of xerostomia was 27%, 14% and 17% at follow-up time points 1, 2 and 3, respectively. At <6 months, the mean dose to the contralateral and the ipsilateral parotid glands (Dmean_contra, Dmean_ipsi) was higher among patients with xerostomia (Dmean_contra: 25 Gy vs. 15 Gy; Dmean_ipsi: 44 Gy vs. 25 Gy). Patients with xerostomia had higher pre-RT WMSFM (3.5 g vs. 2.4 g), and had been treated more frequently with additional chemotherapy (93% vs. 63%; all 4 variables: p < 0.05). At 6-11 months, Dmean_contra among patients with xerostomia was higher compared to patients without (26 Gy vs. 20 Gy). The RR as specified by the one- and two-gland QUANTEC guideline was 2.3 and 1.4 for patients with <6 months follow-up time, and 2.0 and 1.2 for patients with longer follow-up (6-11 + 6-24 months).

Conclusion

Xerostomia following IMRT peaks within six months post-radiotherapy and fades with time. Limiting the mean dose to both parotid glands (ipsilateral <25 Gy, contralateral <25 Gy) and reducing the use of chemotherapy will likely decrease the rate of xerostomia. Both QUANTEC guidelines are effective in preventing xerostomia.

Previous methods to estimate the inherent accuracy of deformable image registration (DIR) have typically been performed relative to a known ground truth, such as tracking of anatomic landmarks or known deformations in a physical or virtual phantom. In this study, we propose a new approach to estimate the spatial geometric uncertainty of DIR using statistical sampling techniques that can be applied to the resulting deformation vector fields (DVFs) for a given registration. The proposed DIR performance metric, the distance discordance metric (DDM), is based on the variability in the distance between corresponding voxels from different images, which are co-registered to the same voxel at location (X) in an arbitrarily chosen 'reference' image. The DDM value, at location (X) in the reference image, represents the mean dispersion between voxels, when these images are registered to other images in the image set. The method requires at least four registered images to estimate the uncertainty of the DIRs, both for inter- and intra-patient DIR. To validate the proposed method, we generated an image set by deforming a software phantom with known DVFs. The registration error was computed at each voxel in the 'reference' phantom and then compared to DDM, inverse consistency error (ICE), and transitivity error (TE) over the entire phantom. The DDM showed a higher Pearson correlation (Rp) with the actual error (Rp ranged from 0.6 to 0.9) in comparison with ICE and TE (Rp ranged from 0.2 to 0.8). In the resulting spatial DDM map, regions with distinct intensity gradients had a lower discordance and therefore, less variability relative to regions with uniform intensity. Subsequently, we applied DDM for intra-patient DIR in an image set of ten longitudinal computed tomography (CT) scans of one prostate cancer patient and for inter-patient DIR in an image set of ten planning CT scans of different head and neck cancer patients. For both intra- and inter-patient DIR, the spatial DDM map showed large variation over the volume of interest (the pelvis for the prostate patient and the head for the head and neck patients). The highest discordance was observed in the soft tissues, such as the brain, bladder, and rectum, due to higher variability in the registration. The smallest DDM values were observed in the bony structures in the pelvis and the base of the skull. The proposed metric, DDM, provides a quantitative tool to evaluate the performance of DIR when a set of images is available. Therefore, DDM can be used to estimate and visualize the uncertainty of intra- and/or inter-patient DIR based on the variability of the registration rather than the absolute registration error.

Background and purpose

To study internal and external generalizability of temporal dose-response relationships for xerostomia after intensity-modulated radiotherapy (IMRT) for head and neck cancer, and to investigate potential amendments of the QUANTEC guidelines.

Material and methods

Objective xerostomia was assessed in 121 patients (n_Cohort1=55; n_Cohort2=66) treated to 70Gy@2Gy in 2006-2015. Univariate and multivariate analyses (UVA, MVA with 1000 bootstrap populations) were conducted in Cohort1, and generalizability of the best-performing MVA model was investigated in Cohort2 (performance: AUC, p-values, and Hosmer-Lemeshow p-values (p_HL)). Ultimately and for clinical guidance, minimum mean dose thresholds to the contralateral and the ipsilateral parotid glands (Dmean_contra, Dmean_ipsi) were estimated from the generated dose-response curves.

Results

The observed xerostomia rate was 38%/47% (3months) and 19%/23% (11-12months) in Cohort1/Cohort2. Risk of xerostomia at 3months increased for higher Dmean_contra and Dmean_ipsi (Cohort1: 0.17·Dmean_contra+0.11·Dmean_ipsi-8.13; AUC=0.90±0.05; p=0.0002±0.002; p_HL=0.22±0.23; Cohort2: AUC=0.81; p<0.0001; p_HL=0.27). The identified minimum Dmean_contra thresholds were lower than in the QUANTEC guidelines (Cohort1/Cohort2: Dmean_contra=12/19Gy; Dmean_contra, Dmean_ipsi=16, 25/20, 26Gy).

Conclusions

Increased Dmean_contra and Dmean_ipsi explain short-term xerostomia following IMRT. Our results also suggest decreasing Dmean_contra to below 20Gy, while keeping Dmean_ipsi to around 25Gy. Long-term xerostomia was less frequent, and no dose-response relationship was established for this follow-up time.

Purpose

As a foundation for a dose escalation trial, we sought to characterize duodenal and non-duodenal small bowel organ motion between fractions of pancreatic radiation therapy.

Patients and methods

Nine patients (4 women, 5 men) undergoing radiation therapy were enrolled in this prospective study. The patients had up to four weekly CT scans performed during their course of radiation therapy. Pancreas, duodenum and non-duodenal small bowel were then contoured for each CT scan. On the initial scan, a four-field plan was generated to fully cover the pancreas. This plan was registered to each subsequent CT scan. Dose-volume histogram (DVH) analyses were performed for the duodenum, non-duodenal small bowel, large bowel, and pancreas.

Results

With significant individual variation, the volume of duodenum receiving at least 80% of the prescribed dose was consistently greater than the remaining small bowel. In the patient with the largest inter-fraction variation, the fractional volume of non-duodenal small bowel irradiated to at least the 80% isodose line ranged from 1% to 20%. In the patient with the largest inter-fraction variation, the fractional volume of duodenum irradiated to at least the 80% isodose line ranged from 30% to 100%.

Conclusion

The volume of small bowel irradiated during four-field pancreatic radiation therapy changes substantially between fractions. This suggests dose escalation may be possible. However, dose limits to the duodenum should be stricter than for other segments of small bowel.

Background

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

Methods

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.

Results

Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10^-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10^-12), and synaptic transmission (P = 6.28 × 10^-8).

Conclusions

Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.