Adaptive volumetric modulated arc treatment planning for esophageal cancers using cone beam computed tomography

A comparative study of the normal oesophageal wall thickness based on 3-dimensional, 4-dimensional, and cone beam computed tomography

BACKGROUND

The study aimed to compare normal oesophageal wall thickness based on 3-dimensional computed tomography (3DCT), 4-dimensional computed tomography (4DCT) and cone beam computed tomography (CBCT).

METHODS

Contrast-enhanced 3DCT, 4DCT, and CBCT scans were acquired from 50 patients with lung cancer or metastatic lung cancer. The outer oesophageal wall was manually contoured on each 3DCT, the maximum intensity projection of 4DCT (4DCTMIP) the end expiration phase of 4DCT (4DCT50) (the end expiration phase of 4DCT) and the CBCT data sets. The average wall thicknesses were measured (defined as R3DCT, R50, RMIP, and RCBCT).

RESULTS

Whether for thoracic or for intra-abdominal segments, there were no significant differences between R3DCT and R50, but significant differences between R3DCT and RMIP, R3DCT and RCBCT. For upper and middle oesophagus, RCBCT were larger than RMIP. There was no significant difference between upper and middle segments on 3DCT, 4DCT, and CBCT. Intra-abdominal oesophageal wall thickness was greater than that of thoracic oesophagus. There were no differences between upper and lower, and middle and lower oesophagus on CBCT.

CONCLUSION

Our findings indicate normal oesophageal wall thickness differed along the length of oesophagus whatever it was delineated on 3DCT, 4DCT (4DCT50 and 4DCTMIP) or CBCT. It is reasonable to use uniform criterion to identify normal esophageal wall thickness when delineating gross tumor volume on 3DCT and 4DCT50, the same is true of delineating internal gross tumor volume on 4DCTMIP or CBCT images for lower and intra-abdominal oesophagus. But, in spite of using contrast-enhanced scanning, relatively blurred boundary on the CBCT images is noteworthy, especially for upper and middle thoracic esophagus.

Volumetric modulated arc therapy versus intensity-modulated proton therapy in neoadjuvant irradiation of locally advanced oesophageal cancer

Background

To investigate the role of intensity-modulated proton therapy (IMPT) compared to volumetric modulated arc therapy (VMAT), realised with RapidArc and RapidPlan methods (RA_RP) for neoadjuvant radiotherapy in locally advanced oesophagal cancer.

Methods

Twenty patients were retrospectively planned for IMPT (with two fields, (IMPT_2F) or with three fields (IMPT_3F)) and RA_RP and the results were compared according to dose-volume metrics. Estimates of the excess absolute risk (EAR) of secondary cancer induction were determined for the lungs. For the cardiac structures, the relative risk (RR) of coronary artery disease (CAD) and chronic heart failure (CHF) were estimated.

Results

Both the RA_RP and IMPT approached allowed to achieve the required coverage for the gross tumour volume, (GTV) and the clinical and the planning target volumes, CTV and PTV (V_98% > 98 for CTV and GTV and V_95% > 95 for the PTV)). The conformity index resulted in 0.88 ± 0.01, 0.89 ± 0.02 and 0.89 ± 0.02 for RA_RP, IMPT_2F and IMPT_3F respectively. With the same order, the homogeneity index for the PTV resulted in 5.6 ± 0.6%, 4.4 ± 0.9% and 4.5 ± 0.8%. Concerning the organs at risk, the IMPT plans showed a systematic and statistically significant incremental sparing when compared to RA_RP, especially for the heart. The mean dose to the combined lungs was 8.6 ± 2.9 Gy for RA_RP, 3.2 ± 1.5 Gy and 2.9 ± 1.2 Gy for IMPT_2F and IMPT_3F. The mean dose to the whole heart resulted to 9.9 ± 1.9 Gy for RA_RP compared to 3.7 ± 1.3 Gy or 4.0 ± 1.4 Gy for IMPT_2F or IMPT_3F; the mean dose to the left ventricle resulted to 6.5 ± 1.6 Gy, 1.9 ± 1.5 Gy, 1.9 ± 1.6 Gy respectively. Similar sparing effects were observed for the liver, the kidneys, the stomach, the spleen and the bowels.

The EAR per 10,000 patients-years of secondary cancer induction resulted in 19.2 ± 5.7 for RA_RP and 6.1 ± 2.7 for IMPT_2F or 5.7 ± 2.4 for IMPT_3F. The RR for the left ventricle resulted in 1.5 ± 0.1 for RA_RP and 1.1 ± 0.1 for both IMPT sets. For the coronaries, the RR resulted in 1.6 ± 0.4 for RA_RP and 1.2 ± 0.3 for protons.

Conclusion

With regard to cancer of the oesophagogastric junction type I and II, the use of intensity-modulated proton therapy seems to have a clear advantage over VMAT. In particular, the reduction of the heart and abdominal structures dose could result in an optimised side effect profile. Furthermore, reduced risk of secondary neoplasia in the lung can be expected in long-term survivors and would be a great gain for cured patients.

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow. Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers. Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation. Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.

A feasibility study of dynamic adaptive radiotherapy for nonsmall cell lung cancer

PURPOSE

The final state of the tumor at the end of a radiotherapy course is dependent on the doses given in each fraction during the treatment course. This study investigates the feasibility of using dynamic adaptive radiotherapy (DART) in treating lung cancers assuming CBCT is available to observe midtreatment tumor states. DART adapts treatment plans using a dynamic programming technique to consider the expected changes of the tumor in the optimization process.

METHODS

DART is constructed using a stochastic control formalism framework. It minimizes the total expected number of tumor cells at the end of a treatment course, which is equivalent to maximizing tumor control probability, subject to the uncertainty inherent in the tumor response. This formulation allows for nonstationary dose distributions as well as nonstationary fractional doses as needed to achieve a series of optimal plans that are conformal to the tumor over time, i.e., spatiotemporally optimal plans. Sixteen phantom cases with various sizes and locations of tumors and organs-at-risk (OAR) were generated using in-house software. Each case was planned with DART and conventional IMRT prescribing 60 Gy in 30 fractions. The observations of the change in the tumor volume over a treatment course were simulated using a two-level cell population model. Monte Carlo simulations of the treatment course for each case were run to account for uncertainty in the tumor response. The same OAR dose constraints were applied for both methods. The frequency of replanning was varied between 1, 2, 5 (weekly), and 29 times (daily). The final average tumor dose and OAR doses have been compared to quantify the potential dosimetric benefits of DART.

RESULTS

The average tumor max, min, mean, and D95 doses using DART relative to these using conventional IMRT were 124.0%-125.2%, 102.1%-114.7%, 113.7%-123.4%, and 102.0%-115.9% (range dependent on the frequency of replanning). The average relative maximum doses for the cord and esophagus, mean doses for the heart and lungs, and D05 for the unspecified tissue resulting 84%-102.4%, 99.8%-106.9%, 66.9%-85.6%, 58.2%-78.8%, and 85.2%-94.0%, respectively.

CONCLUSIONS

It is feasible to apply DART to the treatment of NSCLC using CBCT to observe the midtreatment tumor state. Potential increases in the tumor dose and reductions in the OAR dose, particularly for parallel OARs with mean or dose-volume constraints, could be achieved using DART compared to nonadaptive IMRT.

A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation

PURPOSE

To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.

MATERIALS AND METHODS

90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients' conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. "Inadequate" class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier.

RESULTS

The cohort was affected by PG mean reduction of 23.7±8.8%. During the first 3weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed "inadequate" conditions. 11% of cases showed "bias" due to DIR and script failure; 6% showed "warning" output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases.

CONCLUSIONS

SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.

Use of planar kV vs. CBCT in evaluation of setup errors in oesophagus carcinoma radiotherapy

AIM

The aim of this study is to evaluate differences in terms of the setup errors observed using kV planar image compared to CBCT for oesophageal cancer patients.

BACKGROUND

Planar kV images are quick to acquire but only allow the observation of bony structures. CBCT allows the evaluation of soft tissues, which includes the oesophagus (and tumour) and OAR, giving a more accurate verification of the positioning.

MATERIALS AND METHODS

All patients were imaged with both techniques between January 2012 and March 2014 were included in the study (16 patients, 212 kV images and 116 CBCT images). Differences between the setup errors observed on the two images modalities were studied. A correlation study between TNM staging, tumour location and immobilization systems with setup errors was also done. Finally, the calculation of systematic and random errors allowed to determine the CTV-PTV margin.

RESULTS

A significant discrepancy (p < 0.05) between the setup errors observed with kV and CBCT was observed in the lateral direction. No statistical correlation was found between setup errors and tumour location, immobilization system or TNM staging. The CTV-PTV margin was smaller with CBCT in the vertical (0.6 cm vs. 0.9 cm) and longitudinal (0.7 cm vs. 1 cm) directions and smaller with kV for the lateral directions (0.8 cm vs. 0.9 cm).

CONCLUSIONS

The chosen modality influences the setup error observed which will influence the correction applied. Allowing a better observation of the volumes of interest, CBCT should be the modality of choice in this pathology. The CTV-PTV margins could be shrunk if CBCT is used.

A broad scope knowledge based model for optimization of VMAT in esophageal cancer: validation and assessment of plan quality among different treatment centers

BACKGROUND

To evaluate the performance of a broad scope model-based optimisation process for volumetric modulated arc therapy applied to esophageal cancer.

METHODS AND MATERIALS

A set of 70 previously treated patients in two different institutions, were selected to train a model for the prediction of dose-volume constraints. The model was built with a broad-scope purpose, aiming to be effective for different dose prescriptions and tumour localisations. It was validated on three groups of patients from the same institution and from another clinic not providing patients for the training phase. Comparison of the automated plans was done against reference cases given by the clinically accepted plans.

RESULTS

Quantitative improvements (statistically significant for the majority of the analysed dose-volume parameters) were observed between the benchmark and the test plans. Of 624 dose-volume objectives assessed for plan evaluation, in 21 cases (3.3 %) the reference plans failed to respect the constraints while the model-based plans succeeded. Only in 3 cases (<0.5 %) the reference plans passed the criteria while the model-based failed. In 5.3 % of the cases both groups of plans failed and in the remaining cases both passed the tests.

CONCLUSIONS

Plans were optimised using a broad scope knowledge-based model to determine the dose-volume constraints. The results showed dosimetric improvements when compared to the benchmark data. Particularly the plans optimised for patients from the third centre, not participating to the training, resulted in superior quality. The data suggests that the new engine is reliable and could encourage its application to clinical practice.

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

PURPOSE

Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.

METHODS

1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.

RESULTS

Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.

CONCLUSIONS

Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.

Reduced lung dose during radiotherapy for thoracic esophageal carcinoma: VMAT combined with active breathing control for moderate DIBH

Background

Lung radiation injury is a critical complication of radiotherapy (RT) for thoracic esophageal carcinoma (EC). Therefore, the goal of this study was to investigate the feasibility and dosimetric effects of reducing the lung tissue irradiation dose during RT for thoracic EC by applying volumetric modulated arc radiotherapy (VMAT) combined with active breathing control (ABC) for moderate deep inspiration breath-hold (mDIBH).

Methods

Fifteen patients with thoracic EC were randomly selected to undergo two series of computed tomography (CT) simulation scans with ABC used to achieve mDIBH (representing 80% of peak DIBH value) versus free breathing (FB). Gross tumor volumes were contoured on different CT images, and planning target volumes (PTVs) were obtained using different margins. For PTV_-FB, intensity-modulated radiotherapy (IMRT) was designed with seven fields, and VMAT included two whole arcs. For PTV_-DIBH, VMAT with three 135° arcs was applied, and the corresponding plans were named: IMRT_-FB, VMAT_-FB, and VMAT_-DIBH, respectively. Dosimetric differences between the different plans were compared.

Results

The heart volumes decreased by 19.85%, while total lung volume increased by 52.54% in mDIBH, compared to FB (p < 0.05). The mean conformality index values and homogeneity index values for VMAT_-DIBH (0.86, 1.07) were slightly worse than those for IMRT_-FB (0.90, 1.05) and VMAT_-FB (0.90, 1.06) (p > 0.05). Furthermore, compared to IMRT_-FB and VMAT_-FB, VMAT_-DIBH reduced the mean total lung dose by 18.64% and 17.84%, respectively (p < 0.05); moreover, the V₅, V₁₀, V₂₀, and V₃₀ values for IMRT_-FB and VMAT_-FB were reduced by 10.84% and 10.65% (p > 0.05), 12.5% and 20% (p < 0.05), 30.77% and 33.33% (p < 0.05), and 50.33% and 49.15% (p < 0.05), respectively. However, the heart dose-volume indices were similar between VMAT_-DIBH and VMAT_-FB which were lower than IMRT_-FB without being statistically significant (p > 0.05). The monitor units and treatment time of VMAT_-DIBH were also the lowest (p < 0.05).

Conclusions

VMAT combined with ABC to achieve mDIBH is a feasible approach for RT of thoracic EC. Furthermore, this method has the potential to effectively reduce lung dose in a shorter treatment time and with better targeting accuracy.