Evaluation of Dynamic Tumor-tracking Intensity-modulated Radiotherapy for Locally Advanced Pancreatic Cancer

Population-based asymmetric margins for moving targets in real-time tumor tracking

BACKGROUND

Both geometric and dosimetric components are commonly considered when determining the margin for planning target volume (PTV). As dose distribution is shaped by controlling beam aperture in peripheral dose prescription and dose-escalated simultaneously integrated boost techniques, adjusting the margin by incorporating the variable dosimetric component into the PTV margin is inappropriate; therefore, geometric components should be accurately estimated for margin calculations.

PURPOSE

We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations.

METHODS

A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs.

RESULTS

The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk's formula.

CONCLUSIONS

Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.

Film measurement and analytical approach for assessing treatment accuracy and latency in a magnetic resonance-guided radiotherapy system

PURPOSE

We measure the dose distribution of gated delivery for different target motions and estimate the gating latency in a magnetic resonance-guided radiotherapy (MRgRT) system.

METHOD

The dose distribution accuracy of the gated MRgRT system (MRIdian, Viewray) was investigated using an in-house-developed phantom that was compatible with the magnetic field and gating method. This phantom contains a simulated tumor and a radiochromic film (EBT3, Ashland, Inc.). To investigate the effect of the number of beam switching and target velocity on the dose distribution, two types of target motions were applied. One is that the target was periodically moved at a constant velocity of 5 mm/s with different pause times (0, 1, 3, 10, and 20 s) between the motions. During different pause times, different numbers of beams were switched on/off. The other one is that the target was moved at velocities of 3, 5, 8, and 10 mm/s without any pause (i.e., continuous motion). The gated method was applied to these motions at MRIdian, and the dose distributions in each condition were measured using films. To investigate the relation between target motion and dose distribution in the gating method, we compared the results of the gamma analysis of the calculated and measured dose distributions. Moreover, we analytically estimated the gating latencies from the dose distributions measured using films and the gamma analysis results.

RESULTS

The gamma pass rate linearly decreased with increasing beam switching and target velocity. The overall gating latencies of beam-hold and beam-on were 0.51 ± 0.17 and 0.35 ± 0.05 s, respectively.

CONCLUSIONS

Film measurements highlighted the factors affecting the treatment accuracy of the gated MRgRT system. Our analytical approach, employing gamma analysis on films, can be used to estimate the overall latency of the gated MRgRT system.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

BACKGROUND

In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient's body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion.

METHODS

From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems.

RESULTS

The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively.

CONCLUSIONS

The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset.

Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning

PURPOSE

For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X-ray imaging systems, internal markers must be implemented to facilitate tumor tracking. The purpose of this study was to develop a markerless deep learning-based pancreatic tumor positioning procedure for real-time tumor tracking with a kV X-ray imaging system.

METHODS AND MATERIALS

Fourteen pancreatic cancer patients treated with intensity-modulated radiation therapy from six fixed gantry angles with a gimbal-head radiotherapy system were included in this study. For a gimbal-head radiotherapy system, the three-dimensional (3D) intrafraction target position can be determined using an orthogonal kV X-ray imaging system. All patients underwent four-dimensional computed tomography (4DCT) simulations for treatment planning, which were divided into 10 respiratory phases. After a patient's 4DCT was acquired, for each X-ray tube angle, 10 digitally reconstructed radiograph (DRR) images were obtained. Then, a data augmentation procedure was conducted. The data augmentation procedure first rotated the CT volume around the superior-inferior and anterior-posterior directions from -3^° to 3^° in 1.5^° intervals. Then, the Super-SloMo model was adapted to interpolate 10 frames between respiratory phases. In total, the data augmentation procedure expanded the data scale 250-fold. In this study, for each patient, 12 datasets containing the DRR images from each specific X-ray tube angle based on the radiation therapy plan were obtained. The augmented dataset was randomly divided into training and testing datasets. The training dataset contained 2000 DRR images with clinical target volume (CTV) contours labeled for fine-tuning the pre-trained target contour prediction model. After the fine-tuning, the patient and X-ray tube angle-specific CTV contour prediction model was acquired. The testing dataset contained the remaining 500 images to evaluate the performance of the CTV contour prediction model. The dice similarity coefficient (DSC) between the area enclosed by the CTV contour and predicted contour was calculated to evaluate the model's contour prediction performance. The 3D position of the CTV was calculated based on the centroid of the contour in the orthogonal DRR images, and the 3D error of the prediction position was calculated to evaluate the CTV positioning performance. For each patient, the DSC results from 12 X-ray tube angles and 3D error from 6 gantry angles were calculated, representing the novelty of this study.

RESULTS

The mean and standard deviation (SD) of all patients' DSCs were 0.98 and 0.015, respectively. The mean and SD of the 3D error were 0.29 mm and 0.14 mm, respectively. The global maximum 3D error was 1.66 mm, and the global minimum DSC was 0.81. The mean calculation time for CTV contour prediction was 55 ms per image. This fulfills the requirement of RTTT.

CONCLUSIONS

Regarding the positioning accuracy and calculation efficiency, the presented procedure can provide a solution for markerless real-time tumor tracking for pancreatic cancer patients. This article is protected by copyright. All rights reserved.

Concurrent high-dose intensity-modulated radiotherapy and chemotherapy for unresectable locally advanced and metastatic pancreatic cancer: a pilot study

Aim:

To evaluate the efficacy of concurrent chemotherapy and high-dose (≥55 Gy) intensity-modulated radiotherapy (CCIMRT) in comparison with chemotherapy alone and intensity-modulated radiotherapy (IMRT) alone for unresectable locally advanced or metastatic pancreatic cancer.

Methods:

Forty-six patients with pancreatic cancer undergoing CCIMRT (n = 17), chemotherapy alone (n = 16) or IMRT alone (n = 13) were analysed. Overall survival (OS), locoregional progression-free survival (LRPFS) and gastrointestinal toxicities were evaluated. The median radiation dose was 60 Gy (range, 55–60) delivered in a median of 25 fractions (range, 24–30). Gemcitabine (GEM) alone, GEM + S-1, S-1 alone, FOLFIRINOX and GEM + nab-paclitaxel were used in CCIMRT and chemo-monotherapy.

Results:

The 1-year OS rate was 69% in the CCIMRT group, 27% in the chemotherapy group and 38% in the IMRT group (p = 0·12). The 1-year LRPFS rate was 73, 0 and 40% in the 3 groups, respectively (p = 0·012). Acute Grade ≥ 2 gastrointestinal toxicity (nausea, diarrhea) was observed in 12% (2/17) in the CCIMRT group, 25% (4/16) in the chemotherapy group and 7·7% (1/13) in the IMRT group (p = 0·38). Late Grade 3 gastrointestinal bleeding was observed in 6·3% (1/16) in the chemotherapy group.

Conclusion:

High-dose CCIMRT yielded acceptable toxicity and favorable OS and LRPFS.

Couch and multileaf collimator tracking: A clinical feasibility study for pancreas and liver treatment

PURPOSE

Real-time tumor tracking through active correction by the multileaf collimator or treatment couch offers a promising strategy to mitigate delivery uncertainty due to intrafractional tumor motion. This study evaluated the performance of MLC and couch tracking using the prototype iTools Tracking system in TrueBeam Developer Mode and the application for abdominal cancer treatments.

METHODS

Experiments were carried out using a phantom with embedded Calypso transponders and a motion simulation platform. Geometric evaluations were performed using a circular conformal field with sinusoidal traces and pancreatic tumor motion traces. Geometric tracking accuracy was retrospectively calculated by comparing the compensational MLC or couch motion extracted from machine log files to the target motion reconstructed from real-time MV and kV images. Dosimetric tracking accuracy was measured with radiochromic films using clinical abdominal VMAT plans and pancreatic tumor traces.

RESULTS

Geometrically, the root-mean-square errors for MLC tracking were 0.5 and 1.8 mm parallel and perpendicular to leaf travel direction, respectively. Couch tracking, in contrast, showed an average of 0.8 mm or less geometric error in all directions. Dosimetrically, both MLC and couch tracking reduced motion-induced local dose errors compared to no tracking. Evaluated with five pancreatic tumor motion traces, the average 2%/2 mm global gamma pass rate of eight clinical abdominal VMAT plans was 67.4% (range: 26.4%-92.7%) without tracking, which was improved to 86.0% (range: 67.9%-95.6%) with MLC tracking, and 98.1% (range: 94.9%-100.0%) with couch tracking. In 16 out of 40 deliveries with different plans and motion traces, MLC tracking did not achieve clinically acceptable dosimetric accuracy with 3%/3mm gamma pass rate below 95%.

CONCLUSIONS

This study demonstrated the capability of MLC and couch tracking to reduce motion-induced dose errors in abdominal cases using a prototype tracking system. Clinically significant dose errors were observed with MLC tracking for certain plans which could be attributed to the inferior MLC tracking accuracy in the direction perpendicular to leaf travel, as well as the interplay between motion tracking and plan delivery for highly modulated plans. Couch tracking outperformed MLC tracking with consistently high dosimetric accuracy in all plans evaluated, indicating its clinical potential in the treatment of abdominal cancers.

Direct measurement and correction of both megavoltage and kilovoltage scattered x-rays for orthogonal kilovoltage imaging subsystems with dual flat panel detectors

PURPOSE

To measure the scattered x-rays of megavoltage (MV) and kilovoltage (kV) beams (MV scatter and kV scatter, respectively) on the orthogonal kV imaging subsystems of Vero4DRT.

METHODS

Images containing MV- and kV-scatter from another source only (i.e., MV- and kV-scatter maps) were acquired for each investigated flat panel detector. The reference scatterer was a water-equivalent cuboid phantom. The maps were acquired by changing one of the following parameters from the reference conditions while keeping the others fixed: field size: 10.0 × 10.0 cm² ; dose rate: 400 MU/min; gantry and ring angles: 0°; kV collimator aperture size at isocenter: 10.0 × 10.0 cm² : tube voltage: 110 kV; and exposure: 0.8 mAs. The average pixel values of MV- and kV-scatter (i.e., the MV- and kV-scatter values) at the center of each map were calculated and normalized to the MV-scatter value under the reference conditions (MV- and kV-scatter value factor, respectively). In addition, an MV- and kV-scatter correction experiment with intensity-modulated beams was performed using a phantom with four gold markers (GMs). The ratios between the intensities of the GMs and those of their surroundings were calculated.

RESULTS

The measurements showed a strong dependency of the MV-scatter on the field size and dose rate. The maximum MV-scatter value factors were 2.0 at a field size of 15.0 × 15.0 cm² and 2.5 at a dose rate of 500 MU/min. The maximum kV-scatter value was 0.48 with a fully open kV collimator aperture. In the phantom experiment, the intensity ratios of kV images with MV- and kV-scatter were decreased from the reference ones. After correction of kV-scatter only, MV-scatter only, and both MV- and kV-scatter, the intensity ratios gradually improved.

CONCLUSIONS

MV- and kV-scatter could be corrected by subtracting the scatter maps from the projections, and the correction improved the intensity ratios of the GMs.

The gimbaled-head radiotherapy system: Rise and downfall of a dedicated system for dynamic tumor tracking with real-time monitoring and dynamic WaveArc

A gimbaled-head radiotherapy device was developed by industry-academic collaborations, with a concept of robust structures whilst maintaining high flexibilities, and its clinical application started in 2008. The unique structures with multi-image guidance functions initiated 2 new treatment modalities. One is dynamic tumor tracking radiotherapy with real time monitoring (DTTRM), which enables 4-D radiotherapy without prolongation of radiotherapy treatment time. This treatment has become clinically feasible for stereotactic body radiotherapy (SBRT) of lung cancers and liver tumors, and intensity-modulated radiotherapy (IMRT) for pancreatic cancers. The second one is Dynamic WaveArc therapy (DWA), the non-coplanar versatility of the SBRT system by combining the gantry-ring synchronized rotation with dynamic multileaf collimator optimization. DWA opens the possibility to create patient-individualized treatment plans, allowing additional flexibility in organ at risk sparing while preserving dosimetric robust delivery. The clinical usefulness of the DWA has been preliminary shown for those tumors in the prostate, breast and skull base. Prospective clinical trials are under way with a support of the national funding of Japan for DTTRM and DWA, respectively. Marketing of the system was terminated in 2016 due to a commercial decision. However, lessons can be learned from the development process of this device that might be useful for those who have interests in new technologies and clinical applications in radiation oncology. This review article aims to summarize the developments and achievements of a gimbaled-head radiotherapy device with a focus on DTTRM and DWA.

Radiation therapy for patients with locally advanced pancreatic cancer: Evolving techniques and treatment strategies

Despite ongoing efforts, patients with locally advanced pancreatic cancer (LAPC) continue to have a dismal prognosis. Such tumors are unresectable, and optimal treatment with chemotherapy and/or radiation therapy is still not established. While chemotherapy is conventionally aimed at preventing metastatic spread of disease, radiation therapy acts locally, improving local control which can potentially improve overall survival and most importantly quality of life. Here, we aim to review the primary literature assessing the role of diverse radiation therapy strategies for patients with LAPC. Many radiation regimens can be considered, and no standard treatment has demonstrated a clear improvement in clinical outcomes. We advise that the modality of choice be dependent on the availability of equipment, the dose and fractionation of treatment, as well as the dose received by normal tissue. Moreover, a candid discussion with the patient concerning treatment goals is equally as essential. Three notable strategies for LAPC are intensity-modulated radiation therapy, volumetric modulated arc therapy, and proton. These radiation modalities tend to have improved dose distribution to the target volumes, while minimizing the radiation dose to surrounding normal tissues. Stereotactic body radiation therapy can also be considered in LAPC patients in cases where the tumor does not invade the duodenum or other neighboring structures. Because of the high doses delivered by stereotactic body radiation therapy, proper respiratory and tumor motion management should be implemented to reduce collateral radiation dosing. Despite improved clinical outcomes with modern radiation modalities, evolving techniques, and more accurate planning, future studies remain essential to elucidate the optimal role for radiation therapy among patients with LAPC.

Variation in target volume and centroid position due to breath holding during four-dimensional computed tomography scanning: A phantom study

This study investigated the effects of respiratory motion, including unwanted breath holding, on the target volume and centroid position on four‐dimensional computed tomography (4DCT) imaging. Cine 4DCT images were reconstructed based on a time‐based sorting algorithm, and helical 4DCT images were reconstructed based on both the time‐based sorting algorithm and an amplitude‐based sorting algorithm. A spherical object 20 mm in diameter was moved according to several simulated respiratory motions, with a motion period of 4.0 s and maximum amplitude of 5 mm. The object was extracted automatically, and the target volume and centroid position in the craniocaudal direction were measured using a treatment planning system. When the respiratory motion included unwanted breath‐holding times shorter than the breathing cycle, the root mean square errors (RSME) between the reference and imaged target volumes were 18.8%, 14.0%, and 5.5% in time‐based images in cine mode, time‐based images in helical mode, and amplitude‐based images in helical mode, respectively. In helical mode, the RSME between the reference and imaged centroid position was reduced from 1.42 to 0.50 mm by changing the reconstruction method from time‐ to amplitude‐based sorting. When the respiratory motion included unwanted breath‐holding times equal to the breathing cycle, the RSME between the reference and imaged target volumes were 19.1%, 24.3%, and 15.6% in time‐based images in cine mode, time‐based images in helical mode, and amplitude‐based images in helical mode, respectively. In helical mode, the RSME between the reference and imaged centroid position was reduced from 1.61 to 0.83 mm by changing the reconstruction method from time‐ to amplitude‐based sorting. With respiratory motion including breath holding of shorter duration than the breathing cycle, the accuracies of the target volume and centroid position were improved by amplitude‐based sorting, particularly in helical 4DCT.