Considerable interobserver variation in delineation of pancreatic cancer on 3DCT and 4DCT: a multi-institutional study

Target Contour Consistency During Magnetic Resonance-Guided Online Adaptive Stereotactic Body Radiation Therapy

Purpose

Adaptive magnetic resonance-guided stereotactic body radiation therapy (MRgSBRT) requires expeditious recontouring of target volumes based on daily anatomy. Contouring of the gross tumor volume (GTV) is frequently performed by covering radiation oncologists who may be less familiar with the case than the primary physician (PP). The objective of this study is to determine consistency in GTV contouring between PP and covering physician (CP) and to analyze the effect of resources to support accurate GTV delineation.

Methods and Materials

Between 2021 and 2023, 59 patients underwent 302 fractions of MRgSBRT at our institution. GTVs were analyzed for the effect of 3 different types of contouring support resources: (a) number of slices of the original GTV, (b) external software displaying original GTV contours, and (c) alerting if GTVs differed > 10% from the original. Differences between physicians and contouring support resources were analyzed for different tumor sites and fractions using 2-tailed t test and analysis of variance.

Results

One hundred nineteen out of 302 (39.4%) MRgSBRT treatments were supervised by a CP. The difference in the mean absolute percent volume change of GTV compared with original GTV for PPs (11.1%) versus CPs (4.6%) across all treatment fractions was statistically significant (P = .00006). Significant differences were observed for pancreas (12.8% vs 5.0%, P = .03), liver (13.0% vs 4.0%, P = .007), and lymph nodes (12.4% vs 2.1%, P = .004) with larger volume differences for PPs. No significant differences were observed for tumors of the prostate (3.7% vs 3.6%) and adrenal glands (9.7% vs 12.2%). No significant GTV differences between the 3 contouring support techniques were observed.

Conclusions

Our results show larger GTV changes by PPs for most tumor sites with little impact from contouring support resources. Observed differences might be related to higher contouring confidence of PPs who are more familiar with the case. Further investigation into enhancing contouring support methods is warranted.

Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study

Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This study included 14 hepatopancreatobiliary experts from eight hospitals. The participants assessed three radiologically resectable and three radiologically borderline resectable cases in a simulated setting via crossover design. Groups were divided in controls (using a CT scan), a 3D group (using a CT scan and 3D models), and a CAD group (using a CT scan, 3D and CAD). For the perceived fulfillment of preoperative needs, the quality and confidence of clinical decision-making were evaluated. Results: A higher perceived ability to determine degrees and the length of tumor-vessel contact was reported in the CAD group compared to controls (p = 0.022 and p = 0.003, respectively). Lower degrees of tumor-vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (p = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (p = 0.033) for all cases combined. Conclusions: "CAD (including 3D) improved experts' perceived ability to accurately assess vessel involvement and supports the development of evolving techniques that may enhance the diagnosis and treatment of pancreatic cancer".

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent density models can be utilized to address this problem. The most popular approach is the Probabilistic U-Net (PU-Net), which uses latent Normal densities to optimize the conditional data log-likelihood Evidence Lower Bound. In this work, we demonstrate that the PU-Net latent space is severely sparse and heavily under-utilized. To address this, we introduce mutual information maximization and entropy-regularized Sinkhorn Divergence in the latent space to promote homogeneity across all latent dimensions, effectively improving gradient-descent updates and latent space informativeness. Our results show that by applying this on public datasets of various clinical segmentation problems, our proposed methodology receives up to 11% performance gains compared against preceding latent variable models for probabilistic segmentation on the Hungarian-Matched Intersection over Union. The results indicate that encouraging a homogeneous latent space significantly improves latent density modeling for medical image segmentation.

Impact of motion management strategies on abdominal organ at risk delineation for magnetic resonance-guided radiotherapy

Background and purpose

The impact of respiratory motion management strategies for abdominal radiotherapy, such as abdominal compression (AC) and breath hold (BH), on abdominal organ at risk (OAR) delineation on magnetic resonance imaging (MRI) is unknown. This feasibility study compared the inter- and intra- observer delineation variation on MRI acquired with AC, BH for three critical abdominal OAR.

Materials and methods

T2-weighted (W) 3D MRI in free-breathing (FB) and with AC, and T1W 3D mDixon exhale BH were acquired. Four observers blinded to motion management strategy used, delineated stomach, liver, and duodenum on all MRI. One case per strategy was repeated over 6 weeks later to quantify intra-observer variation. Simultaneous truth and performance level estimation (STAPLE) contours for each OAR were generated, median and IQR mean distance to agreement (mDTA) and maximum Hausdorff distance (HD) between observer and STAPLE contours were calculated. Observers scored organ visibility on each MRI using a four-point Likert scale.

Results

A total of 27 scans including repeats were delineated. Pooled mDTA for all OARs was 1.3 mm (0.5 mm) with AC, 1.4 mm (1.0 mm) with BH, and 1.3 mm (0.5 mm) in FB. Intra-observer mDTA was highest for all organs in FB with 10.8 mm for duodenum, 1.8 mm for liver, and 2.7 mm for stomach. The pooled mean perceptual quality score value was highest for AC across organs.

Conclusions

No motion management strategy demonstrated superior similarity across OAR, emphasizing the need for personalised approaches based on individual clinical and patient factors.

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study

BACKGROUND

The planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast.

METHODS

PANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases.

RESULTS

The median JCI for the arterial and venous phases were 0.50 (range, 0.17-0.64) and 0.41 (range, 0.23-0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38-0.57) on the arterial phase, and 0.52 (range 0.36-0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators.

CONCLUSIONS

Our results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Inter-Observer Variability in MR-Based Target Volume Delineation of Uveal Melanoma

Purpose

Several efforts are being undertaken toward MRI-based treatment planning for ocular proton therapy for uveal melanoma (UM). The interobserver variability of the gross target volume (GTV) on magnetic resonance imaging (MRI) is one of the important parameters to design safety margins for a reliable treatment. Therefore, this study assessed the interobserver variation in GTV delineation of UM on MRI.

Methods and Materials

Six observers delineated the GTV in 10 different patients using the Big Brother contouring software. Patients were scanned at 3T MRI with a surface coil, and tumors were delineated separately on contrast enhanced 3DT1 (T1gd) and 3DT2-weighted scans with an isotropic acquisition resolution of 0.8 mm. Volume difference and overall local variation (median standard deviation of the distance between the delineated contours and the median contour) were analyzed for each GTV. Additionally, the local variation was analyzed for 4 interfaces: sclera, vitreous, retinal detachment, and tumor-choroid interface.

Results

The average GTV was significantly larger on T1gd (0.57cm³) compared with T2 (0.51cm³, P = .01). A not significant higher interobserver variation was found on T1gd (0.41 mm) compared with T2 (0.35 mm). The largest variations were found at the tumor-choroid interface due to peritumoral enhancement (T1gd, 0.62 mm; T2, 0.52 mm). As a result, a larger part of this tumor-choroid interface appeared to be included on T1gd-based GTVs compared with T2, explaining the smaller volumes on T2.

Conclusions

The interobserver variation of 0.4 mm on MRI are low with respect to the voxel size of 0.8 mm, enabling small treatment margins. We recommend delineation based on the T1gd-weighted scans, as choroidal tumor extensions might be missed.

Evaluating differences in respiratory motion estimates during radiotherapy: a single planning 4DMRI versus daily 4DMRI

Background

In radiotherapy, respiratory-induced tumor motion is typically measured using a single four-dimensional computed tomography acquisition (4DCT). Irregular breathing leads to inaccurate motion estimates, potentially resulting in undertreatment of the tumor and unnecessary dose to healthy tissue. The aim of the research was to determine if a daily pre-treatment 4DMRI-strategy led to a significantly improved motion estimate compared to single planning 4DMRI (with or without outlier rejection).

Methods

4DMRI data sets from 10 healthy volunteers were acquired. The first acquisition simulated a planning MRI, the respiratory motion estimate (constructed from the respiratory signal, i.e. the 1D navigator) was compared to the respiratory signal in the subsequent scans (simulating 5–29 treatment fractions). The same procedure was performed using the first acquisition of each day as an estimate for the subsequent acquisitions that day (2 per day, 4–20 per volunteer), simulating a daily MRI strategy. This was done for three outlier strategies: no outlier rejection (NoOR); excluding 5% of the respiratory signal whilst minimizing the range (Min95) and excluding the datapoints outside the mean end-inhalation and end-exhalation positions (MeanIE).

Results

The planning MRI median motion estimates were 27 mm for NoOR, 18 mm for Min95, and 13 mm for MeanIE. The daily MRI median motion estimates were 29 mm for NoOR, 19 mm for Min95 and 15 mm for MeanIE. The percentage of time outside the motion estimate were for the planning MRI: 2%, 10% and 32% for NoOR, Min95 and MeanIE respectively. These values were reduced with the daily MRI strategy: 0%, 6% and 17%. Applying Min95 accounted for a 30% decrease in motion estimate compared to NoOR.

Conclusion

A daily MRI improved the estimation of respiratory motion as compared to a single 4D (planning) MRI significantly. Combining the Min95 technique with a daily 4DMRI resulted in a decrease of inclusion time of 6% with a 30% decrease of motion. Outlier rejection alone on a planning MRI often led to underestimation of the movement and could potentially lead to an underdosage.

Trial registration: protocol W15_373#16.007

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01915-1.

Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer

Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer's delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography

PURPOSE

To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

MATERIALS AND METHODS

Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60 keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60 keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model.

RESULTS

The contrast-to-noise ratio was significantly improved (p < 0.001) in the generated VMIs (4.1 ± 1.8) compared to the SECT images (2.8 ± 1.1). The mean overall image quality (4.1 ± 0.6) and tumor enhancement (3.6 ± 0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p < 0.001) than that in the SECT images (3.2 ± 0.4 and 2.8 ± 0.4 for overall image quality and tumor enhancement, respectively).

CONCLUSIONS

The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.

Survival equivalence in patients treated for borderline resectable and unresectable locally advanced pancreatic ductal adenocarcinoma: a systematic review and network meta-analysis

BACKGROUND

The clinical relevance of subdivision of non-metastatic pancreatic ductal adenocarcinoma (PDAC) into locally advanced borderline resectable (LA-BR) and locally advanced unresectable (LA-UR) has been questioned. We assessed equivalence of overall survival (OS) in patients with LA-BR and LA-UR PDAC.

METHODS

A systematic review was performed of studies published January 1, 2009 to August 21, 2019, reporting OS for LA-BR and LA-UR patients treated with or without neoadjuvant therapy (NAT), with or without surgical resection. A frequentist network meta-analysis was used to assess the primary outcome (hazard ratio for OS) and secondary outcomes (OS in LA-BR, LA-UR, and upfront resectable (UFR) PDAC).

RESULTS

Thirty-nine studies, comprising 14,065 patients in a network of eight unique treatment subgroups were analysed. Overall survival was better for LA-BR than LA-UR patients following surgery both with and without NAT. Neoadjuvant therapy prior to surgery was associated with longer OS for UFR, LA-BR, and LA-UR tumours, compared to upfront surgery.

CONCLUSION

Survival between the LA-BR and LA-UR subgroups was not equivalent. This subdivision is useful for prognostication, but likely unhelpful in treatment decision making. Our data supports NAT regardless of initial disease extent. Individual patient data assessment is needed to accurately estimate the benefit of NAT.

A novel amplitude binning strategy to handle irregular breathing during 4DMRI acquisition: improved imaging for radiotherapy purposes

Background

For radiotherapy of abdominal cancer, four-dimensional magnetic resonance imaging (4DMRI) is desirable for tumor definition and the assessment of tumor and organ motion. However, irregular breathing gives rise to image artifacts. We developed a outlier rejection strategy resulting in a 4DMRI with reduced image artifacts in the presence of irregular breathing.

Methods

We obtained 2D T2-weighted single-shot turbo spin echo images, with an interleaved 1D navigator acquisition to obtain the respiratory signal during free breathing imaging in 2 patients and 12 healthy volunteers. Prior to binning, upper and lower inclusion thresholds were chosen such that 95% of the acquired images were included, while minimizing the distance between the thresholds (inclusion range (IR)). We compared our strategy (Min95) with three commonly applied strategies: phase binning with all images included (Phase), amplitude binning with all images included (MaxIE), and amplitude binning with the thresholds set as the mean end-inhale and mean end-exhale diaphragm positions (MeanIE). We compared 4DMRI quality based on:

Data included (DI); percentage of images remaining after outlier rejection.

Reconstruction completeness (RC); percentage of bin-slice combinations containing at least one image after binning.

Intra-bin variation (IBV); interquartile range of the diaphragm position within the bin-slice combination, averaged over three central slices and ten respiratory bins.

IR.

Image smoothness (S); quantified by fitting a parabola to the diaphragm profile in a sagittal plane of the reconstructed 4DMRI.

A two-sided Wilcoxon’s signed-rank test was used to test for significance in differences between the Min95 strategy and the Phase, MaxIE, and MeanIE strategies.

Results

Based on the fourteen subjects, the Min95 binning strategy outperformed the other strategies with a mean RC of 95.5%, mean IBV of 1.6 mm, mean IR of 15.1 mm and a mean S of 0.90. The Phase strategy showed a poor mean IBV of 6.2 mm and the MaxIE strategy showed a poor mean RC of 85.6%, resulting in image artifacts (mean S of 0.76). The MeanIE strategy demonstrated a mean DI of 85.6%.

Conclusions

Our Min95 reconstruction strategy resulted in a 4DMRI with less artifacts and more precise diaphragm position reconstruction compared to the other strategies.

Trial registration

Volunteers: protocol W15_373#16.007; patients: protocol NL47713.018.14

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1279-z) contains supplementary material, which is available to authorized users.

Respiratory motion of lymph node stations in pancreatic cancer: Analyses using contrast-enhanced four-dimensional computed tomography

BACKGROUND AND PURPOSE

Data regarding respiratory motion of lymph node (LN) stations in pancreatic cancer is limited. Therefore, we assessed their respiratory motion using contrast-enhanced four-dimensional-computed tomography (CE-4DCT).

MATERIAL AND METHODS

We evaluated respiratory motion in 18 pancreatic cancer patients. We selected LN stations around major arteries which were visible on CE-4DCT images. This included the common hepatic, celiac, splenic, and superior mesenteric stations. Two radiation oncologists individually delineated the gross tumor volume (GTV) and the LN stations as observers 1 and 2.

RESULTS

The respiratory motion of the celiac (median, 3.9 mm each for both observers) and superior mesenteric (median, 4.5 and 5.0 mm for observers 1 and 2, respectively) stations in the craniocaudal (CC) directions was significantly smaller than that of the GTV (median, 8.9 and 7.8 mm for observers 1 and 2, respectively). The respiratory motion of the common hepatic station (median, 3.8 and 3.6 mm for observers 1 and 2, respectively) in the anterior-posterior (AP) direction was significantly larger than that of the GTV (median, 2.8 and 2.2 mm for observers 1 and 2, respectively).

CONCLUSIONS

We observed significant differences in respiratory motion between the GTV and the LN stations in pancreatic cancer.

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman’s rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.

Dosimetric effects of anatomical changes during fractionated photon radiation therapy in pancreatic cancer patients

Pancreatic tumors show large interfractional position variation. In addition, changes in gastrointestinal gas volumes and body contour take place over the course of radiation therapy. We aimed to quantify the effect of these anatomical changes on target dose coverage, for the clinically used fiducial marker‐based patient position verification and, for comparison, also for simulated bony anatomy‐based position verification. Nine consecutive patients were included in this retrospective study. To enable fraction dose calculations on cone‐beam CT (CBCT), the planning CT was deformably registered to each CBCT (13–15 per patient); gas volumes visible on CBCT were copied to the deformed CT. Fraction doses were calculated for the clinically used 10 MV VMAT treatment plan (with for the planning target volume (PTV): D_98% = 95%), according to fiducial marker‐based and bony anatomy‐based image registrations. Dose distributions were rigidly summed to yield the accumulated dose. To evaluate target dose coverage, we defined an iCTV_{+5 mm} volume, i.e., the internal clinical target volume (iCTV) expanded with a 5 mm margin to account for remaining uncertainties including delineation uncertainties. We analyzed D_98%, D_mean, and D_2% for iCTV_{+5 mm} and PTV (i.e., iCTV plus 10 mm margin). We found that for fiducial marker‐based registration, differences between fraction doses and planned dose were minimal. For bony anatomy‐based registration, fraction doses differed considerably, resulting in large differences between planned and accumulated dose for some patients, up to a decrease in D_98% of the iCTV_{+5 mm} from 95.9% to 85.8%. Our study shows that fractionated photon irradiation of pancreatic tumors is robust against variations in body contour and gastrointestinal gas, with dose coverage only mildly affected. However, as a result of interfractional tumor position variations, target dose coverage can severely decline when using bony anatomy for patient position verification. Therefore, the use of intratumoral fiducial marker‐based daily position verification is essential in pancreatic cancer patients.

Addition of MRI for CT-based pancreatic tumor delineation: a feasibility study

PURPOSE

To assess the effect of additional magnetic resonance imaging (MRI) alongside the planning computed tomography (CT) scan on target volume delineation in pancreatic cancer patients.

MATERIAL AND METHODS

Eight observers (radiation oncologists) from six institutions delineated the gross tumor volume (GTV) on 3DCT, and internal GTV (iGTV) on 4DCT of four pancreatic cancer patients, while MRI was available in a second window (CT + MRI). Variations in volume, generalized conformity index (CI_gen), and overall observer variation, expressed as standard deviation (SD) of the distances between delineated surfaces, were analyzed. CI_gen is a measure of overlap of the delineated iGTVs (1 = full overlap, 0 = no overlap). Results were compared with those from an earlier study that assessed the interobserver variation by the same observers on the same patients on CT without MRI (CT-only).

RESULTS

The maximum ratios between delineated volumes within a patient were 6.1 and 22.4 for the GTV (3DCT) and iGTV (4DCT), respectively. The average (root-mean-square) overall observer variations were SD = 0.41 cm (GTV) and SD = 0.73 cm (iGTV). The mean CI_gen was 0.36 for GTV and 0.37 for iGTV. When compared to the iGTV delineated on CT-only, the mean volumes of the iGTV on CT + MRI were significantly smaller (32%, Wilcoxon signed-rank, p < .0005). The median volumes of the iGTV on CT + MRI were included for 97% and 92% in the median volumes of the iGTV on CT. Furthermore, CT + MRI showed smaller overall observer variations (root-mean-square SD = 0.59 cm) in six out of eight delineated structures compared to CT-only (root-mean-square SD = 0.72 cm). However, large local observer variations remained close to biliary stents and pathological lymph nodes, indicating issues with instructions and instruction compliance.

CONCLUSIONS

The availability of MRI images during target delineation of pancreatic cancer on 3DCT and 4DCT resulted in smaller target volumes and reduced the interobserver variation in six out of eight delineated structures.

Quality assurance of the PREOPANC trial (2012-003181-40) for preoperative radiochemotherapy in pancreatic cancer : The dummy run

Background

The Dutch Pancreatic Cancer Group initiated the national, multicentre, controlled PREOPANC trial, randomising between preoperative radiochemotherapy and direct explorative laparotomy for patients with (borderline) resectable pancreatic cancer. The aim of this dummy run is to evaluate compliance with the radiotherapy protocol of this trial, and the quality of delineation and radiation plans.

Methods

Eleven radiation oncology departments open for accrual of patients in the PREOPANC trial were provided with all necessary information of a selected ‘dummy’ patient. Each institute was asked to delineate the target volumes, including gross tumour volume, internal gross tumour volume (iGTV), internal clinical target volume, and planning target volume. The institutions were also asked to provide a radiation treatment plan in accordance with the PREOPANC trial protocol.

Results

The range of the iGTV was 19.3–77.2 cm³ with a mean iGTV of 41.5 cm³ (standard deviation 14.8 cm³). Nine institutions made a treatment plan using an arc technique for treatment delivery, one an intensity modulated technique and one a 3-field conformal technique. All institutions reached the prescribed target coverage, without exceeding the organs at risk constraints. The institution with the 3‑field conformal technique was advised to use a more sophisticated technique (e. g. volumetric modulated arc therapy) to reduce the dose to the spinal cord.

Conclusion

All institutions showed acceptable deviations from the PREOPANC trial protocol and achieved an acceptable quality of delineation and radiation technique. All institutions were allowed to continue participation in the PREOPANC trial.

Electronic supplementary material

The online version of this article (doi:10.1007/s00066-017-1153-6) contains supplementary material, which is available to authorized users. Supplementary material: PREOPANC Protocol, version 11, radiotherapy part