A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

Johnson, Corinne; Price, Gareth J; Khalifa, J.; Faivre-Finn, Corinne; Dekker, André; Moore, Christopher; Herk, Marcel van

doi:10.1016/j.radonc.2017.11.015

Cited by 11 publications

(6 citation statements)

References 109 publications

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“…We believe the two techniques complement each other, and consideration should be made on which approach to adopt depending on the dataset and purpose. The method developed by Johnson et al was validated in 15 patients to provide accurate volume estimates for larger volume tumors, but was not consistently validated for shape sensitive parameters (i.e., DTA) so the development of a method for shape estimation was encouraged 16 . We have developed a technique that provides accurate volume and boundary estimates for early‐stage lung tumors, not typically attached to rigid structures or invading mediastinum 44 .…”

Section: Discussionmentioning

confidence: 99%

“…So far, Johnson et al proposed the only method to generate a GTV from an iGTV. 16 Erosion kernels were applied independently to upper and lower lobe lung tumors, derived from average difference between iGTV and GTV for 25 tumors of varied size and location. Importantly, the training cohort had a larger average tumor size than tumors typically treated with SABR.…”

Section: Introductionmentioning

confidence: 99%

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Automated gross tumor volume contour generation for large‐scale analysis of early‐stage lung cancer patients planned with 4D‐CT

et al. 2020

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Purpose: Patients with early-stage lung cancer undergoing stereotactic ablative radiotherapy receive four-dimensional computed tomography (4D-CT) for treatment planning. Often, an internal gross target volume (iGTV), which approximates the motion envelope of a tumor over the breathing cycle, is delineated without defining a gross tumor volume (GTV). However, the GTV volume and shape are important parameters for prognostic and dose modelling, and there is interest in radiomic features extracted from the GTV and surrounding tissue. We demonstrate and validate a method to generate the GTV from an iGTV contour to aid retrospective analysis on routine data. Method: It is possible to reconstruct the geometry of a tumor with knowledge of tumor motion and the motion envelope formed during respiration. To demonstrate this, the tumor motion path was estimated with local rigid registration, and the iGTV positioned incrementally at stations along the reverse path. It is shown that the tumor volume is the largest set common to the intersection of the iGTV at these positions, hence can be derived. This was implemented for 521 lung lesions on 4D-CT. Eleven patients with a GTV delineation performed by a radiation oncologist on a reference phase (50%) were used for validation. The generated GTV was compared to that delineated by the expert using distance-to-agreement (DTA), volume, and distance between centres of mass. An overall success rate was determined by detecting registration inaccuracy and performing a quality check on the routine iGTV. For successfully generated contours, GTV volume was compared to iGTV volume in a prognostic model for overall survival. Results: For the validation dataset, DTA mean (0.79-1.55 mm) and standard deviation (0.68-1.51 mm) were comparable to expected observer variation. Difference in volume was < 5 cm 3 , and average difference in position was 1.21 mm. Deviations in shape and position were mainly caused by observer differences in iGTV and GTV interpretation as opposed to algorithm performance. For the complete dataset, an acceptable contour was generated for 94% of patients using statistical and visual assessment to detect failures. Generated GTV volumes improved prognostic model performance over iGTV volumes. Conclusion: A method to generate a GTV from an iGTV and 4D-CT dataset was developed. This method facilitates data analysis of patients with early-stage lung cancer treated in the routine setting, that is, data mining, prognostic modeling, and radiomics. Generation failure detection removes the need for visual assessment of all contours, reducing a time-consuming aspect of big-data analysis. Favorable prognostic performance of generated GTV volumes over iGTV ones demonstrates opportunities to use this methodology for future study.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated gross tumor volume contour generation for large‐scale analysis of early‐stage lung cancer patients planned with 4D‐CT

et al. 2020

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“…Gandevia and Stradling first reported this problem in the fifties of the twentieth century [26]. In recent years, there have been many studies on the differences between observers and computers in the field of radiotherapy [27,28]. Our study did not analyze the parameter differences of synchronous respiratory tracking output resulting from different observers' perception of tracking volume boundaries.…”

Section: Disease Markersmentioning

confidence: 93%

Optimized CyberKnife Lung Treatment: Effect of Fractionated Tracking Volume Change on Tracking Results

Wang

Qiu

et al. 2020

Disease Markers

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Objectives. To explore the impact of volume change in the fractionated tracking of stereotactic radiotherapy on the results of synchronous, respiratory tracking algorithm using CyberKnife. Methods. A total of 38 lung tumor patients receiving stereotactic radiotherapy at our center from March 2018 to October 2019 were counted. Photoshop CS4 image processing software was used to obtain the pixels and the average value of brightness of the tracking volume in the image and calculate the grayscale within the contour of the tracking volume on the real-time X-ray image. At the same time, parameters of the synchronous respiratory tracking algorithm of the fractional CyberKnife were extracted for comparison between the volume of image-guided image tracking and the number of fractions during stereotactic radiotherapy. We also analyzed the relationship between fraction tumor location and characteristics and the calculated results of synchronous respiratory tracking by CyberKnife. Results. There were no significant differences between the first four fractions (p>0.05) for left lung lesions and no significant differences between the first five fractions for right lung lesions (p≥0.05). For peripheral lung cancer, longer fractional treatment led to greater variation in grayscale (G-A: >4 fractions p<0.05), while for central lung cancer, longer fractional treatment led to greater variation in parameters of the synchronous respiratory tracking algorithm (Uncertainty A and Uncertainty B: >4 fractions p<0.05). There was a significant correlation between radiotherapy-graded tumor density and relevant parameters, and the correlation was strong (>0.7, p<0.05). Conclusion. With the increase of treatment fractions, the gray value in the patient tracking volume decreased. Patients of >4 fractions were advised to reevaluate with simulated CT and replan. For tumors with small diameter and low density, the imaging changes of volume should be closely followed during treatment. For left lung and central lung cancer, carefully select the synchronous tracking treatment with 2-view.

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“…Univariable analysis of any relationship between clinical variables and the vector shift to the heart was undertaken via Pearson correlation and Analysis of Variance. Tested clinical variables included: age, gender, ECOG performance status (ECOG-PS), overall stage, t-stage, the natural logarithm of the Gross Tumour Volume (GTV, estimated from the motion-adapted GTV, using the method described by Johnson et al [16]), fractionation scheme, the time between planning and treatment and ACE-27 scale comorbidities. The resulting p-values were adjusted using the Benjamini and Hochberg False Discovery Rate (FDR) method [17], to correct for the effects of multiple comparisons.…”

Section: Methodsmentioning

confidence: 99%

The impact of baseline shifts towards the heart after image guidance on survival in lung SABR patients

Johnson-Hart

Price

Osorio

et al. 2020

Radiotherapy and Oncology

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Highlights Baseline shifts in the direction of the heart are significantly related to overall survival. There is an increased risk with increasing shifts towards the heart. Shifts away from the heart reduce the risk, suggesting survival can be improved. We recommend stricter heart dose constraints for lung SABR treatment planning.

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A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

Abstract: Accurate synthesis of GTV size from the iGTV permits the combination of lung cancer patient cohorts, facilitating machine learning applications in thoracic radiotherapy.

Cited by 11 publications

References 109 publications

Automated gross tumor volume contour generation for large‐scale analysis of early‐stage lung cancer patients planned with 4D‐CT

Automated gross tumor volume contour generation for large‐scale analysis of early‐stage lung cancer patients planned with 4D‐CT

Optimized CyberKnife Lung Treatment: Effect of Fractionated Tracking Volume Change on Tracking Results

The impact of baseline shifts towards the heart after image guidance on survival in lung SABR patients

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