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Background Respiratory motion may introduce errors during radiotherapy. This study aims to assess and validate internal gross tumour volume (IGTV) margins in proximal and distal borders of gastroesophageal junction (GEJ) tumours during simultaneous integrated boost radiotherapy. Methods We enrolled 10 patients in group A and 9 patients in group B. For all patients, two markers were placed at the upper and lower borders of the tumour before treatment. In group A, within the simulation and every 5 fractions of radiotherapy, we used 4-dimensional computed tomography (4DCT) to record the intrafractional displacement of the proximal and distal markers. By fusing the average image of each repeated 4DCT with the simulation image based on the lumbar vertebra, the interfractional displacement could be obtained. We calculated the IGTV margin in the proximal and distal borders of the GEJ tumour. In group B, by referring to the simulation images and cone-beam computed tomography (CBCT) images, the range of tumour displacement in proximal and distal borders of GEJ tumour was estimated. We calculated the proportion of marker displacement range in group B lay within the IGTV margin calculated based on the data obtained in group A to estimate the accuracy of the IGTV margin. Results The intrafractional displacement in the cranial–caudal (CC) direction was significantly larger than that in the anterior–posterior (AP) and left–right (LR) directions for both the proximal and distal markers of the tumour. The interfractional displacement in the AP and LR directions was larger than that in the CC direction (p = 0.001, p = 0.017) based on the distal marker. The IGTV margins in the LR, AP and CC directions were 9 mm, 8.5 mm and 12.1 mm for the proximal marker and 15.8 mm, 12.7 mm and 11.5 mm for the distal marker, respectively. In group B, the proportions of markers that located within the IGTV margin in the LR, AP and CC directions were 96.5%, 91.3% and 96.5% for the proximal marker and 100%, 96.5%, 93.1% for the distal marker, respectively. Conclusions Our study proposed individualized IGTV margins for proximal and distal borders of GEJ tumours during neoadjuvant radiotherapy. The IGTV margin determined in this study was acceptable. This margin could be a reference in clinical practice.

BACKGROUND AND PURPOSE: Tumour growth during radiotherapy may lead to geographical misses of the target volume. This study investigates the evolution of the tumour extent and evaluates the need for plan adaptation to ensure dose coverage of the target in glioblastoma patients.MATERIALS AND METHODS: The prospective study included 29 patients referred for 59.4 Gy in 33 fractions. Magnetic resonance imaging (MRI) was performed at the time of treatment planning, at fraction 10, 20, 30, and three weeks after the end of radiotherapy. The gross tumour volume (GTV) was defined as the T1w contrast-enhanced region plus the surgical cavity on each MRI set. The relative GTV volume and the maximum distance (Dmax) of the extent of the actual GTV outside the original GTV were measured. Based on the location of the actual GTV during radiotherapy and the original planned dose, a prospective clinical decision was made whether to adapt the treatment.RESULTS: Dose coverage of the GTV during radiotherapy was not compromised, and none of the radiotherapy plans was adapted. The median Dmax (range) was 5.7 (2.0-18.9) mm, 8.0 (2.0-27.4) mm, 8.0 (1.9-27.3) mm, and 8.9 (1.9-34.4) mm at fraction 10, 20, 30, and follow-up. The relative GTV volume and Dmax observed at fraction 10 were correlated with the values observed at follow-up (R=0.74, pCONCLUSION: Large variations in the GTV extent were observed, and changes often occurred early in the treatment. Plan adaptation for geographical misses was not performed in our cohort due to sufficient CTV margins.

Highlights • Deep learning accelerates 4D-MRI recon for online adaptive MR-guided radiotherapy. • First reconstruction of high resolution whole thorax 4D-MRI for 16 phases in 28 s. • First use of dCNNs for midposition reconstruction from undersampled 4D-MRI in 28 s. • Excellent agreement between deep learning-based tumour midposition and reference., Background and Purpose 4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. Methods Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. Results For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. Conclusion Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

Background and purpose To compare 68Ga-fibroblast activation protein inhibitor (FAPI) and 18F-FDG PET/CT in imaging locally advanced oesophageal cancer, and evaluate the potential usefulness of 68Ga-FAPI PET/CT on gross target volume (GTV) delineation aimed at radiotherapy planning for oesophageal cancer as compared with contrast-enhanced CT (CE-CT) and 18F-FDG PET/CT. Materials and methods Twenty-one patients with newly diagnosed oesophageal cancer who underwent both 18F-FDG and 68Ga-FAPI PET/CT scans were selected. GTVs of the primary tumours based on CE-CT (GTVCT), PET/CT, and CE-CT plus PET/CT were delineated. Gross tumour lengths were measured by GTVs and endoscopy and recorded. Results The 68Ga-FAPI PET showed significantly higher radiotracer uptake than 18F-FDG PET (median SUVmax 16.71 vs. 11.23; P = 0.002) in the primary tumours. SUV thresholds of FAPI ×20%, 30%, 40%, and FDG ×40% showed similar lesion lengths compared with that in endoscopic examination (P > 0.05). GTVCT demonstrated the largest volume (median: 48.80 mm3, range: 14.83–162.23 mm3) than PET-based GTVs. For PET/CT-guided complementary contouring of GTVCT, four patients (19%) were increased by FAPI ×20% and 30%, two patients (9.5%) were increased by FAPI ×40%, and only one patient was increased by FDG ×40%. Furthermore, the volume of GTV based on CE-CT plus FAPI ×20%, 30%, and 40% showed no significant difference with GTVCT and planning target volume based CE-CT plus FAPI-PET and meets the organ at risk standard. Conclusion The 68Ga-FAPI PET/CT methodology showed favourable tumour-to-background contrast in oesophageal cancer and might provide additional information for target volume delineation and help avoid tumour geographic misses.

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.

Contains fulltext : 235315.pdf (Publisher’s version ) (Closed access) PURPOSE: This study proposes optimal tracer-specific threshold-based window levels for PSMA PET-based intraprostatic gross tumour volume (GTV) contouring to reduce interobserver delineation variability. METHODS: Nine (68)Ga-PSMA-11 and nine (18)F-PSMA-1007 PET scans including GTV delineations of four expert teams (GTV(manual)) and a majority-voted GTV (GTV(majority)) were assessed with respect to a registered histopathological GTV (GTV(histo)) as the gold standard reference. The standard uptake values (SUVs) per voxel were converted to a percentage (SUV%) relative to the SUV(max). The statistically optimised SUV% threshold (SOST) was defined as those that maximises accuracy for threshold-based contouring. A leave-one-out cross-validation receiver operating characteristic (ROC) curve analysis was performed to determine the SOST for each tracer. The SOST analysis was performed twice, first using the GTV(histo) contour as training structure (GTV(SOST-H)) and second using the GTV(majority) contour as training structure (GTV(SOST-MA)) to correct for any limited misregistration. The accuracy of both GTV(SOST-H) and GTV(SOST-MA) was calculated relative to GTV(histo) in the 'leave-one-out' patient of each fold and compared with the accuracy of GTV(manual). RESULTS: ROC curve analysis for (68)Ga-PSMA-11 PET revealed a median threshold of 25 SUV% (range, 22-27 SUV%) and 41 SUV% (40-43 SUV%) for GTV(SOST-H) and GTV(SOST-MA), respectively. For (18)F-PSMA-1007 PET, a median threshold of 42 SUV% (39-45 SUV%) for GTV(SOST-H) and 44 SUV% (42-45 SUV%) for GTV(SOST-MA) was found. A significant pairwise difference was observed when comparing the accuracy of the GTV(SOST-H) contours with the median accuracy of the GTV(manual) contours (median, - 2.5%; IQR, - 26.5-0.2%; p = 0.020), whereas no significant pairwise difference was found for the GTV(SOST-MA) contours (median, - 0.3%; IQR, - 4.4-0.6%; p = 0.199). CONCLUSIONS: Threshold-based contouring using GTV(majority)-trained SOSTs achieves an accuracy comparable with manual contours in delineating GTV(histo). The median SOSTs of 41 SUV% for (68)Ga-PSMA-11 PET and 44 SUV% for (18)F-PSMA-1007 PET form a base for tracer-specific window levelling. TRIAL REGISTRATION: Clinicaltrials.gov ; NCT03327675; 31-10-2017.

Aim: Colorectal carcinomas are one of the most common carcinomas in the Western world. Survival is mainly associated with the tumour-node-metastasis (TNM) stage but patients with the same tumour stage usually show marked distinct survival. We analyzed the survival effect of gross tumour volume and poorly differentiated clusters in pT1-2 rectal carcinomas. Material and Method: Sixty-five pT1-2 rectal carcinomas that were curatively resected between 1999 and 2014 were included in this retrospective study at Kırıkkale University Medical Faculty Hospital. Gross tumour volume and poorly differentiated clusters were scored using a macroscopic specimen and hematoxylin and eosin-stained sections. Results: These parameters were significantly associated with large tumour size (gross tumour volume [GTV]: p=0.020), invasive pattern (GTV: p=0.004; poorly differentiated clusters [PDC]: p=0.020), angiolymphatic invasion (GTV: p=0.001; PDC: p=0.009), tumour necrosis (GTV: p=0.002; PDC: p=0.038), and high grade (PDC: p=0.001). In univariate analysis, patients with these parameters had worse 5-year survival for both relapse-free survival (RFS) and overall survival (OS) ([GTV: RFS= 78.5%, p=0.001; OS: 81.0%, p=0.005], [PDC: RFS= 80.0%, p=0.013; OS: 83.1%, p=0.039]). Multivariate analysis confirmed that these parameters are independent predictors of poor survival for RFS (GTV: Hazard ratio [HR]=1.42 [1.06-2.85], p=0.006; PDC: HR=1.39 [1.06-3.28], p=0.028) and OS (GTV: HR=1.35 [1.09-3.37], p=0.011). Also, GTV was found to be more useful than PDC.Conclusions: According to our study, GTV and PDC play an important role in the prognosis of rectal carcinomas and the addition of these markers to the current risk classification may contribute to better patient selection.

Aim:The aim of the study was to find the dosimetric impact of positron emission tomography (PET)-based gross tumour volume (GTV) delineation over computed tomography (CT)-based GTV delineation for carcinoma oesophagus.Methods:Fifteen patients with carcinoma oesophagus were retrospectively selected. Two sets of GTVs in CT plain images were generated, one with the help of intravenous and oral contrast (GTV CT) and the other with only using PET uptake with the standardised uptake value (simple way of determining the activity in PET) (SUV) > 2.5 (GTV PET). Corresponding PTVs were generated. For all patients, rapid arc plans were generated. Changes in target volumes and critical structure doses were evaluated. The Wilcoxon signed-rank test was used for statistical analysis, and p value < 0.05 was assumed as statistically significant.Results:Mean reduction in GTV was 5.76 ± 19.35 cc. Mean reduction in PTV 45 Gy was 42.40 ± 76.39 cc. Mean reduction in heart mean dose was 1.53 ± 2.16 Gy. Mean reductions in left lung V20% and V10% were 2.43 ± 4.28 and 3.25 ± 5.09 Gy, respectively. Mean reductions in right lung V20% and V10% were 3.11 ± 4.91 and 2.80 ± 4.51 Gy, respectively. Mean reduction in total lung mean dose was 1.00 ± 1.19 Gy.Finding:PET-based GTV contouring reduces the treatment volume and critical structure doses significantly over CT-based GTV contouring for carcinoma oesophagus.

Aim:Magnetic resonance imaging (MRI) is indispensable for treatment planning in prostate radiotherapy (PR). Registration of MRI when compared to planning CT (pCT) is prone to uncertainty and this is rarely reported. In this study, we have compared three different types of registration methods to justify the direct use of MRI in PR.Methods and materials:Thirty patients treated for PR were retrospectively selected for this study and all underwent both CT and MRI. The MR scans were registered to the pCT using markers, focused and unfocussed methods and their registration are REGM, REGF, and REGNF, respectively. Registration comparison is done using the translational differences of three axes from the centre-of-mass values of gross tumour volume (GTV) generated using MRI.Results:The average difference in all three axes (x, y, z) is (1, 2·5, 2·3 mm) and (1, 3, 2·3 mm) for REGF-REFNF and REGF-REGM, respectively. MR-based GTV Volume is less in comparison to CT-based GTV and it is significantly different (p < 0·001).Findings:Image registration uncertainty is unavoidable for a regular CT–MR workflow. Additional planning target volume margin ranging from 2 to 3mm could be avoided if MR-only workflow is employed. This reduction in the margin is beneficial for small tumours treated with hypofractionation.

Purpose To compare gross tumour volume (GTV) in oropharynx carcinomas using different intelligent imaging software and to evaluate which method is more reliable for tumour volume definition in comparison with 3D ProSoma software. Material and methods We retrospectively studied 32 patients with histopathologically confirmed oropharynx carcinomas on dual-source computed tomography (CT) (all patients underwent multislice CT examination after applying 75 ml iodinated non-ionic contrast media). One radiologist calculated the tumour volume - manually measuring tumour length (L), width (W), and height (H) - and then calculated the tumour volume using the formula 0.5236 × L × W × H. The other radiologist used the syngo.CT-Liver-Analysis software to calculate the tumour volumes. Both volume measuring methods were compared with the 3D ProSoma software, which is used by radiotherapists to calculate tumour volumes. Graphpad Prism software was used for statistical data. Results syngo.CT-Liver-Analysis software for gross tumour volume determination has greater reliability than the standard manual method with Syngo Plaza in comparison with the 3D ProSoma software. Conclusions syngo.CT-Liver-Analysis software is a reliable tool for GTV calculation, with a high correlation score, like that of radiotherapeutic 3D ProSoma software.

// Chao-Yue Hu 1, 2 , Jian-Bin Li 2 , Jin-Zhi Wang 2 , Wei Wang 2 , Feng-Xiang Li 2 and Yan-Luan Guo 2 1 School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, Shandong Province, China 2 Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong Province, China Correspondence to: Jian-Bin Li, email: lijianbin@msn.com Keywords: oesophageal cancer, three-dimensional computed tomography, four-dimensional computed tomography, cone beam computed tomography, gross tumor volume Received: April 13, 2017 Accepted: September 19, 2017 Published: October 05, 2017 ABSTRACT Background: To explore motion information included in 3DCT, 4DCT and CBCT by comparing volumetric and positional differences of GTV. Results: Independent of tumor location, significant differences were observed among volumes [IGTV 10 > (IGTV CBCT or IGTV MIP ) > (GTV 3D or GTV 4D50 )]. The underestimations or overestimations between IGTV 10 and IGTV CBCT were larger than those between IGTV 10 and IGTV MIP ( p < 0.001–0.011; p < 0.001–0.023). For upper oesophageal tumors, GTV 4D50 /IGTV CBCT negatively correlated with motion vector (r = –0.756, p = 0.011). In AP direction, the centroid coordinates of IGTV CBCT differed from GTV 3D , GTV 4D50 , IGTV MIP and IGTV 10 ( p = 0.006, 0.013, 0.038, and 0.010). For middle oesophageal tumors, IGTV 10 /IGTV CBCT positively correlated with motion vector (r = 0.695, p = 0.006). The centroid coordinates of IGTV CBCT differed from those of IGTV 10 ( p = 0.046) in AP direction. For distal oesophageal tumors, the centroid coordinates of IGTV CBCT showed significant differences to those of IGTV MIP ( p = 0.042) in LR direction. For both middle and distal tumors, the degrees of associations of IGTV 10 outside IGTV CBCT significantly correlated with the motion vector (r = 0.540, p = 0.046; r = 0.678, p = 0.031). Materials and Methods: Thirty-four oesophageal cancer patients underwent 3DCT, 4DCT and CBCT. GTV 3D , GTV 4D50 , internal GTV MIP (IGTV MIP ) and IGTV CBCT were delineated on 3DCT, 4DCT 50 , 4DCT MIP and CBCT. GTVs from 10 respiratory phases were combined to produce GTV 10 . Differences in volume, position for different targets, correlation between volume ratio and motion vector were evaluated. The motion vector was the spatial moving of the target centroid position. Conclusions: IGTV CBCT encompasses more motion information than GTV 3D and GTV 4D50 for upper oesophageal tumors, but slightly less than IGTV 10 for middle and distal oesophageal tumors. IGTV CBCT incorporated similar motion information to IGTV MIP . However, motion information encompassed in CBCT and MIP cannot replace each other.

Background/aim To evaluate the utility of high spatial resolution digital positron emission tomography images with the source-to-background ratio (SBR) algorithm for gross tumour volume (GTV) delineation. Materials and methods The bowl and spheres (10-37 mm) were filled with fluoro-2-deoxy-D-glucose to achieve 4-16 times background radioactivity. The images were reconstructed using three isotropic voxel sizes. The SBR and percentage threshold (TH) to SUVmax were calculated. The plots between SBR and TH were fitted using a regression equation. The contoured volumes (CVs) of the spheres were calculated by applying TH. Results TH was 38.6+75.0/SBR for 4 mm voxel size; 39.6+37.0/SBR for 2 mm; and 38.8+35.2/SBR for 1 mm. The mean relative errors between CV and true volume for 4, 2, and 1 mm voxel sizes were 15%, 7%, and 7%, respectively. Conclusion The present technique is useful for GTV delineation with reduced contouring error.

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTV entire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTV entire&nbsp, was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 &plusmn, 0.04 (mean &plusmn, std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 &plusmn, 0.02 and 0.64 &plusmn, 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 &plusmn, 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.

The aim of this study was to evaluate the impact of actual rotational setup errors on dose distributions in intracranial stereotactic radiotherapy (SRT) with different alternatives for treatment position selection. A total of 38 SRT fractions from 18 patients were retrospectively evaluated with rotational setup errors obtained from actual treatments. The planning computed tomography (CT) images were rotated according to online cone‐beam CT (CBCT) images and the dose distribution was recalculated to the rotated CT images using three different patient positionings derived from: 1) an automatic 6D match neglecting rotation correction (Auto6D); 2) an automatic 3D match (Auto3D); and 3) a manual 3D match from actual treatment (Treat3D). The mean conformity index (CI) was 0.92 for the original plans and 0.91 for the Auto6D plans. The mean CI decreased significantly (p0.38) was found in the Auto3D and the Treat3D cases between the rotation error and CI, PTVmin or minimum dose of gross tumour volume. In SRT, a treatment plan of comparable quality to 6D rotation correction can be achieved by using 6D registration without a rotational correction in the selection of patient positioning. This was demonstrated for typical rotation errors seen in clinical practice. PACS number(s): 87.55, 87.57

In this study, we propose a novel approach to investigate changes in the visible tumour and surrounding tissues with the aim of identifying patterns of tumour change during radiotherapy (RT) without segmentation on the follow-up images. On-treatment cone-beam computed tomography (CBCT) images of 240 non-small cell lung cancer (NSCLC) patients who received 55 Gy of RT were included. CBCTs were automatically aligned onto planning computed tomography (planning CT) scan using a two-step rigid registration process. To explore density changes across the lung-tumour boundary, eight shells confined to the shape of the gross tumour volume (GTV) were created. The shells extended 6 mm inside and outside of the GTV border, and each shell is 1.5 mm thick. After applying intensity correction on CBCTs, the mean intensity was extracted from each shell across all CBCTs. Thereafter, linear fits were created, indicating density change over time in each shell during treatment. The slopes of all eight shells were clustered to explore patterns in the slopes that show how tumours change. Seven clusters were obtained, 97% of the patients were clustered into three groups. After visual inspection, we found that these clusters represented patients with little or no density change, progression and regression. For the three groups, the survival curves were not significantly different between the groups, p-value = 0.51. However, the results show that definite patterns of tumour change exist, suggesting that it may be possible to identify patterns of tumour changes from on-treatment CBCT images.

Highlights • Interobserver variability in delineation of the oesophageal GTV can be considerable. • Delineation variation is mainly located at the cranial and caudal border. • PET significantly influences the delineated GTV in oesophageal cancer. • The impact of PET to CT on observer variation of the GTV is limited. • Accurate GTV delineation is essential for results of radiation boost-strategies., Background and purpose Accurate delineation of the primary tumour is vital to the success of radiotherapy and even more important for successful boost strategies, aiming for improved local control in oesophageal cancer patients. Therefore, the aim was to assess delineation variability of the gross tumour volume (GTV) between CT and combined PET-CT in oesophageal cancer patients in a multi-institutional study. Materials and methods Twenty observers from 14 institutes delineated the primary tumour of 6 cases on CT and PET-CT fusion. The delineated volumes, generalized conformity index (CIgen) and standard deviation (SD) in position of the most cranial/caudal slice over the observers were evaluated. For the central delineated region, perpendicular distance between median surface GTV and each individual GTV was evaluated as in-slice SD. Results After addition of PET, mean GTVs were significantly smaller in 3 cases and larger in 1 case. No difference in CIgen was observed (average 0.67 on CT, 0.69 on PET-CT). On CT cranial-caudal delineation variation ranged between 0.2 and 1.5 cm SD versus 0.2 and 1.3 cm SD on PET-CT. After addition of PET, the cranial and caudal variation was significantly reduced in 1 and 2 cases, respectively. The in-slice SD was on average 0.16 cm in both phases. Conclusion In some cases considerable GTV delineation variability was observed at the cranial-caudal border. PET significantly influenced the delineated volume in four out of six cases, however its impact on observer variation was limited.

Purpose To derive personalized tumour control probability (TCP) models, using diffusion-weighted (DW-) MRI for defining initial tumour cellular density in skull-base chordoma patients undergoing carbon-ion radiotherapy (CIRT). Materials and methods 67 patients affected by skull-base chordoma were enrolled for a standardized CIRT treatment (70.4 Gy (RBE) prescription dose). Local control information was clinically assessed. For 20 of them, apparent diffusion coefficient (ADC) maps were computed from DW-MRI and then converted into cellular density. Radiosensitivity parameters (α, β) were estimated from the available data through an optimization procedure, taking advantage of a relationship observed between local control and the dose received by at least the 98% of the gross tumour volume. These parameters were fed into two poissonian TCP models, based on the LQ model, being the first (TCPLIT) computed from literature parameters and the second (TCPADC) enriched by a personalized initial cellular density derived from ADC maps. Results The inclusion of the cellular density derived from ADC into TCPADC yielded slightly higher dose values at which TCP = 0.5 (D50 = 38.91 Gy (RBE)) with respect to TCPLIT (D5034.16 Gy (RBE)). This suggested a more conservative approach, even if the prognostic power of TCPADC and TCPLIT, tested with respect to local control, was equivalent in terms of sensitivity (0.867) and specificity (0.600). Conclusions Both TCPADC and TCPLIT exhibited good agreement with a clinically validated information of local control, the former providing more conservative predictions.

Introduction: Inter-observer variability (IOV) in target volume delineation is a source of error in head and neck radiotherapy. Diffusion-weighted imaging (DWI) has been shown to be useful in detecting recurrent head and neck cancer. This study aims to determine whether DWI improves target volume delineation and IOV. Methods: Four radiation oncologists delineated the gross tumour volume (GTV) for ten head and neck cancer patients. Delineation was performed on CT alone as well as fused image sets which incorporated fluorodeoxyglucose (FDG)-positron emission tomography (PET) and magnetic resonance imaging (MRI) in the form of CT/PET, CT/PET/T2W and CT/PET/T2W/DWI image sets. Analysis of the variability of contour volumes was completed by comparison to the simultaneous truth and performance level estimation (STAPLE) volumes. The DICE Similarity Coefficient (DSC) and other IOV metrics for each observer's contour were compared to the STAPLE for each patient and image dataset. A DWI usability scoresheet for delineation was completed. Results: The CT/PET/T2W/DWI mean GTV volume of 13.37 (10.35–16.39)cm was shown to be different to the mean GTV of 10.92 (8.32–13.51)cm when using CT alone (P < 0.001). The GTV DSC amongst observers for CT alone was 0.72 (0.65–0.79), CT/PET was 0.73 (0.67–0.80), CT/PET/T2W was 0.71 (0.64–0.77) and CT/PET/T2W/DWI was 0.69 (0.61–0.75). Conclusion: Mean GTVs with the addition of DWI had slightly larger volumes compared to standard CT and CT/PET volumes. DWI may add supplemental visual information for GTV delineation while having a small impact on IOV, therefore potentially improving target volume delineation.

Our objective was to investigate direct voxel-wise relationship between dose and early MR biomarker changes both within and in the high-dose region surrounding brain metastases following stereotactic radiosurgery (SRS). Specifically, we examined the apparent diffusion coefficient (ADC) from diffusion-weighted imaging and the contrast transfer coefficient (Ktrans) and volume of extracellular extravascular space (ve) derived from dynamic contrast-enhanced (DCE) MRI data. We investigated 29 brain metastases in 18 patients using 3 T MRI to collect imaging data at day 0, day 3 and day 20 following SRS. The ADC maps were generated by the scanner and Ktrans and ve maps were generated using in-house software for dynamic tracer-kinetic analysis. To enable spatially-correlated voxel-wise analysis, we developed a registration pipeline to register all ADC, Ktrans and ve maps to the planning MRI scan. To interrogate longitudinal changes, we computed absolute ΔADC, ΔKtrans and Δve for day 3 and 20 post-SRS relative to day 0. We performed a Kruskall-Wallice test on each biomarker between time points and investigated dose correlations within the gross tumour volume (GTV) and surrounding high dose region > 12 Gy via Spearman's rho. Only ve exhibited significant differences between day 0 and 20 (p < 0.005) and day 3 and 20 (p < 0.05) within the GTV following SRS. Strongest dose correlations were observed for ADC within the GTV (rho = 0.17 to 0.20) and weak correlations were observed for ADC and Ktrans in the surrounding > 12 Gy region. Both ΔKtrans and Δve showed a trend with dose at day 20 within the GTV and > 12 Gy region (rho = -0.04 to -0.16). Weak dose-related decreases in Ktrans and ve within the GTV and high dose region at day 20 most likely reflect underlying vascular responses to radiation. Our study also provides a voxel-wise analysis schema for future MR biomarker studies with the goal of elucidating surrogates for radionecrosis.