Preplanning prediction of the left anterior descending artery maximum dose based on patient, dosimetric, and treatment planning parameters

Association between the cardiac contact distance and the maximum dose at the left anterior descending coronary artery in post mastectomized patients

The clinical information on the relationship between the cardiac contact distance (CCD), the maximum dose (D_max) delivered to the left anterior descending (LAD) coronary artery and the mean heart dose has mostly focused on patients with breast-conserving surgery (BCS), being scarce in postmastectomy patients. The aim of this study is to determine the association between the CCD and the D_max delivered to the LAD. The secondary objective was to evaluate the dosimetric results of comparing three-dimensional conformal radiotherapy (3D-CRT) to intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) techniques for post mastectomized breast cancer patients with irradiation to the left chest wall. 53 cases of women who received adjuvant standard fractionated postmastectomy radiotherapy (PMRT) were used. Three types of plans were created for each patient: 3D-CRT, seven equidistant IMRT fields, and four partial VMAT arcs. Correlations were evaluated using Pearson's correlation coefficient. Plans made with IMRT and VMAT showed improved homogeneity and conformity. Associations between CCD and D_max to LAD were positive for all three plan types. Compared to 3D-CRT, the modulated intensity plans obtained better dose homogeneity and conformity to the target volume. The LAD and heart doses were significantly lower for IMRT and VMAT plans. The CCD can be used as a predictor of the maximum and mean doses of the LAD. Modulated intensity techniques allow for better dose distribution and dose reduction to the heart and LAD.

NCCN Guidelines® Insights: Hodgkin Lymphoma, Version 2.2022

Hodgkin lymphoma (HL) is an uncommon malignancy of B-cell origin. Classical HL (cHL) and nodular lymphocyte-predominant HL are the 2 main types of HL. The cure rates for HL have increased so markedly with the advent of modern treatment options that overriding treatment considerations often relate to long-term toxicity. These NCCN Guidelines Insights discuss the recent updates to the NCCN Guidelines for HL focusing on (1) radiation therapy dose constraints in the management of patients with HL, and (2) the management of advanced-stage and relapsed or refractory cHL.

Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors

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Extraction, analysis, and interpretation of historical treatment planning data is valuable but very time-consuming.

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Proposed machine learning model classifies radiotherapy plans based on their treatment planning objectives and trade-offs.

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Application of double nested cross-validation enabled to build a robust model that achieved 94% accuracy on a testing data.

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Model reasoning investigated with SHAP values showed consistency with clinical observations.

Purpose

To create and investigate a novel, clinical decision-support system using machine learning (ML).

Methods and Materials

The ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross-validation and an area-under-the-curve (AUC) metric, with the highest performing one selected for further investigation. The model predictions were explained with Shapely additive explanation (SHAP) interaction values.

Results

The highest-performing model was Logistic Regression achieving an accuracy of 93.8 ± 4.1% and AUC of 0.98 ± 0.02 on the testing data. The SHAP analysis indicated that the ΔD_99% metric for PTV had the greatest influence on the model predictions. The least important feature was ΔD_MAX for the left and right cochleae.

Conclusions

The trained model achieved satisfactory accuracy and can be used by medical physicists in a data-driven quality assurance program as well as by radiation oncologists to support their decision-making process in terms of treatment plan approval and potential plan modifications. Model explanation analysis showed that the model relies on clinically valid logic when making predictions.

Knowledge-based radiation treatment planning: A data-driven method survey

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Whole breast external beam radiotherapy in elderly patients affected by left-sided early breast cancer: a dosimetric comparison between two simple free-breathing techniques

BACKGROUND

Elderly breast cancer patients are frequently affected by significant comorbidities that make sophisticated radiotherapy treatments particularly challenging.

AIMS

We dosimetrically analyzed two different simple free-breathing external beam radiotherapy (EBRT) techniques for the hypofractionated treatment of the left breast in elderly patients with a low compliance, to compare target coverage, and heart and left anterior descending coronary artery (LADCA) sparing.

METHODS

We developed radiation plans for 24 elderly patients using 3D conformal (3DCRT) field-in-field tangential technique and intensity-modulated (IMRT) tangential beam technique. Dose-Volume-Histograms (DVHs) were used to provide a quantitative comparison between plans.

RESULTS

The median breast volume was 645 cm³. IMRT and 3DCRT plans comparison demonstrated no significant differences in terms of organ sparing for the heart. Regarding LADCA, mean dose (10.3 ± 9.5 Gy vs 11.9 ± 9.6 Gy, p = 0.0003), maximum dose (26.1 ± 16.1 Gy vs 29.1 ± 16.1 Gy, p = 0.004) and V_{17 Gy} (21.5% ± 26.9% vs 25.0% ± 27.2%, p = 0.002) significantly decreased using IMRT compared with 3DCRT. IMRT plans showed a better target coverage compared with 3DCRT (0.91 ± 0.05 vs 0.93 ± 0.04, p = 0.05).

DISCUSSION

Comparing the two different EBRT techniques, we demonstrated few, although substantial, dosimetric differences in terms of doses to the organs at risk characterized by a statistically significant dose reduction of LADCA in the IMRT plans.

CONCLUSIONS

Elderly patients with a low compliance to treatment might benefit from 3DCRT with field-in-field tangential arrangement or from a simple IMRT approach. IMRT should be preferred.

Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches

Purpose

Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results.

Methods

In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here.

Results

The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation.

Conclusions

The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.

Model-based cardiac dose estimation in radiation treatment of left breast cancer

OBJECTIVE:

To develop a mathematical model for cardiac dose estimation for patients who have been treated for left-sided breast cancer without CT data.

METHODS:

After obtaining institutional review board approvals, 147 patients with left-sided breast cancer were selected that were treated supine with opposed tangents. The heart blocks from the tangential fields were removed and dose calculations were performed with 6 MV beams using an advanced algorithm. This study was performed with CT data with DRR to represent a radiographic simulator image of yesteryear treatments. The beam's eye-view images showing delineated breast, lung and heart were created to represent views on radiograph. The maximum heart distance (MHD) was recorded and irradiated heart area (AREA) was computed by combination of triangles and rectangles. Based on accurate 3D dose calculation, mean dose (D_mean) and V₁₀ to V₅₀ of heart were analyzed with respect to MHD and AREA for dosimetric parameters using linear and quadratic fit.

RESULTS:

The treatment parameters calculated by MHD and segments using 2D radiographs were within 2% of the actual dosimetric parameters computed from the 3D planning system. The MHD and AREA vs D_mean, V₁₀, V₂₀, V₃₀ and V₅₀ showed very good correlation with linear model (R² > 0.91); however the correlation was significantly better with quadratic model (R² > 0.92). The analysis of the dosimetric error with our linear and quadratic model is remarkable within <3% error for most cases.

CONCLUSION:

The proposed mathematical model for the cardiac dose estimation is accurate within ±3% using a radiograph without CT data. This provides avenues for patient pooling in future studies related to radiation dose and cardiac toxicity. These results will help in estimating cardiac dose analysis accurately from previous studies as well as in centers still using 2D planning.

ADVANCES IN KNOWLEDGE:

The evidence of cardiac risk following radiotherapy continues to be one of the important considerations for the management of left-sided breast cancer patients. One of the problem in the estimation of dose-risk effects is the reconstruction of heart dose for pre-CT treatments. In this study, a simple mathematical model is presented that could estimate cardiac dose within ±3% in left breast cancer treatment from 2D radiograph where CT data do not exist.