Adaptive radiation therapy: When, how and what are the benefits that literature provides?

Optimal Timing of Organs-at-risk-sparing Adaptive Radiation Therapy for Head-and-neck Cancer under Re-planning Resource Constraints

Background and Purpose

Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-plannings, which represent a one-size-fits-all approach and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, specifically for patients with head and neck cancer (HNC).

Methods and Materials

A novel Markov decision process (MDP) model was developed to determine optimal timing of re-plannings based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated at the University of Texas MD Anderson Cancer Center between 2007 and 2013. Optimal replanning strategies were obtained when the permissible number of re-plannings throughout the treatment was limited to 1, 2, and 3.

Results

The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3).

Conclusion

The MDP model determines the optimal timing for implementing patient-specific adaptive re-planning. This approach incorporates ΔNTCP thresholds and considers varying total re-plannings. The methods are versatile and applicable across cancer types, institutional settings, and different OARs and NTCP models.

The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.

Solution for the External Contour Changes in Cone Beam Computed Tomography-Guided On-demand Online Adaptive Radiotherapy for a Patient With Very Advanced Head and Neck Cancer: A Technical Case Report

This article presents a case of a patient with advanced head and neck cancer, characterized by a large and protruding tumor. The patient was treated with an innovative on-demand online adaptive radiotherapy (ART) technology, guided by cone beam computed tomography (CBCT), on the Ethos adaptive radiotherapy platform (version 1.0, Varian Medical Systems, Palo Alto, CA). A solution was provided for this special case to address the issue where part of the target volume could not participate in the optimization due to exceeding the external contour boundary during online adaptive radiotherapy. The treatment outcome was satisfactory in terms of tumor regression, while only grade 1 radiodermatitis and grade 2 oral mucositis at the end of radiotherapy. This article discusses the clinical diagnosis, treatment process, and follow-up of this case, aiming to provide clinical references for a broader application of this technology.

Adaptive radiotherapy for head and neck cancer: Pitfalls and possibilities from the radiation oncologist's point of view

BACKGROUND

Patients with head and neck cancer (HNC) may experience substantial anatomical changes during the course of radiotherapy treatment. The implementation of adaptive radiotherapy (ART) proves effective in managing the consequent impact on the planned dose distribution.

METHODS

This narrative literature review comprehensively discusses the diverse strategies of ART in HNC and the documented dosimetric and clinical advantages associated with these approaches, while also addressing the current challenges for integration of ART into clinical practice.

RESULTS AND CONCLUSION

Although based on mainly non-randomized and retrospective trials, there is accumulating evidence that ART has the potential to reduce toxicity and improve quality of life and tumor control in HNC patients treated with RT. However, several questions remain regarding accurate patient selection, the ideal frequency and timing of replanning, and the appropriate way for image registration and dose calculation. Well-designed randomized prospective trials, with a predetermined protocol for both image registration and dose summation, are urgently needed to further investigate the dosimetric and clinical benefits of ART.

In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy

Purpose

Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting.

Methods and Materials

Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan.

Results

In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment.

Conclusions

For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.

Adaptive radiotherapy of locally advanced sigmoid colon cancer with intra‑fractional motion using the MRIdian system: A case report

Neoadjuvant chemotherapy, when combined with radiotherapy, serves as an optional treatment for patients with locally advanced sigmoid colon cancer and is usually performed in conjunction with complete mesocolic excision. The substantial movement of surrounding organs in cases of sigmoid colon cancer frequently leads to toxicity in normal tissues. The present report details the case of a 76-year-old man diagnosed with locally advanced sigmoid colon cancer. Initially, treatment using the Tomotherapy Hi-Art system was selected; however, during image guidance from the first to the sixth fractions, the tumor location underwent a marked change, exceeding the range of the planning target volume. Efforts to recapture the image were unsuccessful, leading to a decision to transition the patient to the MRIdian system for daily treatment with online adaptive radiotherapy. The positional variations in the tumor were evident in each treatment using the MRIdian system, with mean shifts of 2.58 cm in the right-left direction, 1.24 cm in the cranial-caudal direction and 0.40 cm in the anterior-posterior direction. The mean time from the entry of the patient to treatment completion was 41 min. Adaptive treatment plans were performed for all 19 fractions, with two treatments repeated due to the tumor moving out of tracking range. Following irradiation using the MRIdian system, the gross tumor volume decreased by 62%. Notably, the patient experienced no side effects during treatment. A CT scan conducted 3 months after radiotherapy revealed a marked reduction in the tumor size, consistent with a partial response, leading to the scheduling of surgery. Following surgery, a CT scan after 6 months revealed no local recurrence in the surgical bed region. The findings in the present case support the feasibility of implementing an adaptive treatment plan using the MRIdian system for locally advanced sigmoid colon cancer in the context of neoadjuvant chemoradiotherapy.

Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancer†

In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem; however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy; thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .

Evaluation of morphological changes based on cone beam CT for adaptive radiotherapy

BACKGROUND

During radiotherapy treatment, morphological changes can occur in patients (as weight loss). This can lead to significant dosimetric consequences on target volumes and/or organs at risk. The process of adaptive radiotherapy can compensate for these variations. Its deployment in the clinic is slowed by the considerable additional workload for the medical teams. The need for a tool facilitating the detection of patients whose treatment plans need adaptation has been clearly expressed in clinical practice, hence the usefulness of studying the impact of these morphological changes before the decision of adaptive radiotherapy.

METHODS

We considered the cases of 26 patients treated for pelvic cancer where CBCT (Cone Beam Computed Tomography) repositioning images were used. These images have undergone pre-processing to improve their quality and obtain a more precise registration using seven algorithms. We compared the results obtained in order to choose the most adequate algorithm allowing the calculation of external morphological differences using similarity metrics, such as DSC, NCC, MI and TC.

RESULTS

In this study, we showed that the "rigid body" algorithm, based on the rigid registration, gives the best results. The conservation of external contours allowed quantification of the variation in the external volumes of the patients. The obtained variations were on average (6.12±1.69)% and (4.36±1.22)% for rectum and prostate cancers, respectively.

CONCLUSION

Morphological changes evaluated in this study may influence the quality of patient treatment; hence the need for adaptive radiotherapy to take these variations into consideration. However, a rigorous evaluation of the dosimetric impact of these morphological variations is necessary to determine decision criteria for treatment plan adaptation.

Radiation Therapy Changed the Second Malignancy Pattern in Rectal Cancer Survivors

Background and Objectives: Radiotherapy (RT) plays an important role in the treatment for locally advanced rectal cancer patients. It can bring radio exposure together with the survival benefit. Cancer survivors are generally at an increased risk for second malignancies, and survivors receiving RT may have higher risks than survivors not receiving RT. Whether the risk of an all-site second malignancy may increase after RT is still debated. This study aims to compare the second malignancy pattern in rectal cancer survivors after RT. Materials and Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used for analysis. In total, 49,961 rectal cancer patients (20-84 years of age) were identified between 2000 and 2012 from 18 SEER registries. All patients underwent surgery. The occurrence of second malignancies diagnosed after rectal cancer diagnosis was compared in patients who received and did not receive RT. The standardized incidence ratios (SIRs) with 95% confidence intervals (CIs) were used. SEER*Stat was used to generate the 95% CIs for the SIR statistics using the exact method. Results: Of the total 49,961 patients, 5582 developed second malignancies. For all-site second primary malignancies, the age-adjusted SIRs were 1.14 (95% CI 1.1-1.18) and 1.00 (95% CI 0.96-1.04) in the no RT and RT groups, respectively. In 23,192 patients from the surgery-only group, 2604 had second malignancies, and in 26,769 patients who received RT, 2978 developed second malignancies. With respect to every site, the risk of secondary prostate cancer was significantly lower in the RT group (SIR = 0.39, 95% CI 0.33-0.46) than that in the surgery-only group (SIR = 1.04, 95% CI 0.96-1.12). Moreover, the risk of thyroid cancer was significantly higher in the RT group (SIR = 2.80, 95% CI 2.2-3.51) than that in the surgery-only group (SIR = 1.29, 95% CI 0.99-1.66). Conclusions: RT may change the second malignancy pattern in rectal cancer survivors; the risk of prostate cancer decreased, and the risk of thyroid cancer increased most significantly.

Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics-An Urgent Alliance for the Front Line of the War against Head and Neck Cancers

The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by "in house" simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the "next level" of HNC adaptive radiotherapy (ART).

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach

Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.