Gross Tumor Volume Research Articles

Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations.

Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations. Five pancreatic cancer patients were enrolled in this study. Following the guidelines of AAPM TG-132, a patient-specific quality assurance (QA) workflow was developed to evaluate DIR for the abdomen using the TG-132 recommended virtual simulation software ImSimQA (Shrewsbury, UK). First, the planning CT was deformed to simulate respiratory motion using the embedded biomechanical model in ImSimQA. Additionally, 5mm translational motion was added to the stomach, duodenum, and small bowel. The original planning CT and the deformed CT were then imported into Eclipse and MIM to perform DIR. The output displacement vector fields (DVFs) were compared with the ground truth from ImSimQA. Furthermore, the original treatment plan was recalculated on the ground-truth deformed CT and the deformed CT (with Eclipse and MIM DVF). The dose errors were calculated on a voxel-to-voxel basis. Data analysis comparing DVF from Eclipse versus MIM show the average mean DVF magnitude errors of 2.8±1.0 versus 1.1±0.7mm for stomach and duodenum, 5.2±4.0 versus 2.5±1.0mm for small bowel, and 4.8±4.1 versus 2.7±1.1mm for the gross tumor volume (GTV), respectively, across all patients. The mean dose error on stomach+duodenum and small bowel were 2.3±0.6% for Eclipse, and 1.0±0.3% for MIM. As the DIR magnitude error increases, the dose error range increase, for both Eclipse and MIM. In our study, an initial assessment was conducted to evaluate the accuracy of DIR and its dosimetric impact on radiotherapy. A patient-specific DIR QA workflow was developed for pancreatic cancer patients. This workflow exhibits promising potential for future implementation as a clinical workflow.

Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency

Background and PurposeTo evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring. Materials and MethodsNine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared. ResultsUse of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used. ConclusionsA DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency.

Effect of primary tumor volume on survival of concurrent chemoradiotherapy in stage IV non-small cell lung cancer.

To explore the survival effect of thoracic gross tumor volume (GTV) in three-dimensional (3D) radiotherapy for stage IV non-small cell lung cancer (NSCLC). The data cases were obtained from a single-center retrospective analysis. From May. From 2008 to August 2018, 377 treatment criteria were enrolled. GTV was defined as the volume of the primary lesion and the hilus as well as the mediastinal metastatic lymph node. Chemotherapy was a platinum-based combined regimen of two drugs. The number of median chemotherapy cycles was 4 (2-6), and the cut-off value of the planning target volume (PTV) dose of the primary tumor was 63 Gy (30-76.5 Gy). The cut-off value of GTV volume was 150 cm3 (5.83-3535.20 cm3). The survival rate of patients with GTV <150 cm3 is better than patients with GTV ≥150 cm3. Multivariate Cox regression analyses suggested that peripheral lung cancer, radiation dose ≥63 Gy, GTV <150 cm3, 4-6 cycles of chemotherapy, and CR + PR are good prognostic factors for patients with stage IV non-small cell lung cancer. The survival rate of patients with GTV <150 cm3 was longer than patients with ≥150 cm3 when they underwent 2 to 3 cycles of chemotherapy concurrent 3D radiotherapy (p < 0.05). When performing 4 to 6 cycles of chemotherapy concurrent 3D radiotherapy, there was no significant difference between <150 cm3 and ≥150 cm3. The volume of stage IV NSCLC primary tumor can affect the survival of patients. Appropriate treatment methods can be opted by considering the volume of tumors to extend patients' lifetime to the utmost.

Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50.

Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%. A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study

PurposeWe aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).MethodsA total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysisResultsThe pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively.ConclusionsThe radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

Feasibility of using synthetic MRI to predict lymphatic vascular space invasion status in early-stage cervical cancer: added value to morphological MRI

To investigate the feasibility of synthetic magnetic resonance imaging (syMRI) in predicting the lymphatic vascular space invasion (LVSI) status of early-stage cervical cancer, and its added value to morphological MRI. A total of 72 patients with pathology-confirmed early-stage cervical cancer were enrolled, and classified into LVSI- positive (n=41) and LVSI- negative (n=31) groups. Together with morphological parameters including gross tumor volume (GTV) and maximum tumor diameter (MTD), the T1, T2, and proton density (PD) values of the tumors were also measured and compared between two groups. Binary logistic regression analysis was used to identify the independent variable associated with LVSI. Receiver operating characteristic curve analyses and DeLong tests were used to evaluate and compare the performances of significant parameters or their combination in predicting LVSI. LVSI- positive group showed significantly higher GTV (P=0.008) and MTD (P=0.019), and lower T1 (P<0.001) and PD values (P=0.041) than LVSI- negative group. However, no statistical significance was observed regarding the T2 values (P=0.331). Binary logistic regression indicated that T1 value (odds ratio [OR]=0.993; P=0.001) and MTD (OR=1.903, P=0.027) were independent variables associated with LVSI in early cervical cancer. Optimal performance could be achieved [area under ROC curve (AUC)=0.784; cut-off value=0.56; sensitivity=80.5%; specificity=71.0%] when combining T1 and MTD for predicting LVSI. Its performance was significantly better than that of MTD alone (AUC, 0.784 vs 0.662, P=0.035). syMRI might be a feasible approach, and it can provide added value to morphological MRI in predicting the LVSI status of early-stage cervical cancer.

Impact of breathing signal-guided dose modulation on step-and-shoot 4DCT image reconstruction.

Breathing signal-guided 4DCT sequence scanning such as the intelligent 4DCT (i4DCT) approach reduces imaging artifacts compared to conventional 4DCT. By design, i4DCT captures entire breathing cycles during beam-on periods, leading to redundant projection data and increased radiation exposure to patients exhibiting prolonged exhalation phases. A recently proposed breathing-guided dose modulation (DM) algorithm promises to lower the imaging dose by temporarily reducing the CT tube current, but the impact on image reconstruction and the resulting images have not beeninvestigated. We evaluate the impact of breathing signal-guided DM on 4DCT image reconstruction and correspondingimages. This study is designed as a comparative and retrospective analysis based on 104 4DCT datasets. Each dataset underwent retrospective reconstruction twice: (a) utilizing the acquired clinical projection data for reconstruction, which yields reference image data, and (b) excluding projections acquired during potential DM phases from image reconstruction, resulting in DM-affected image data. Resulting images underwent automatic organ segmentation (lung/liver). (Dis)Similarity of reference and DM-affected images were quantified by the Dice coefficient of the entire organ masks and the organ overlaps within the DM-affected slices. Further, for lung cases, (a) and (b) were deformably registered and median magnitudes of the obtained displacement field were computed. Eventually, for 17 lung cases, gross tumor volumes (GTV) were recontoured on both (a) and (b). Target volume similarity was quantified by the Hausdorffdistance. DM resulted in a median imaging dose reduction of 15.4% (interquartile range [IQR]: 11.3%-19.9%) for the present patient cohort. Dice coefficients for lung ( ) and liver ( ) patients were consistently high for both the entire organs and the DM-affected slices (IQR lung: [entire lung/DM-affected slices only] to ; IQR liver: to ), demonstrating that DM did not cause organ distortions or alterations. Median displacements for DM-affected to reference image registration varied; however, only two out of 73 cases exhibited a median displacement larger than one isotropic 1 voxel size. The impact on GTV definition for the end-exhalation phase was also minor (median Hausdorff distance: 0.38mm, IQR: 0.15-0.46mm). This study demonstrates that breathing signal-guided DM has a minimal impact on image reconstruction and image appearance while improving patient safety by reducing doseexposure.

Multifraction stereotactic radiotherapy utilizing inhomogeneous dose distribution for brainstem metastases: a single-center retrospective analysis.

Brainstem metastases are challenging to manage owing to the critical neurological structures involved. Although stereotactic radiotherapy (SRT) offers targeted high doses while minimizing damage to adjacent normal tissues, the optimal dose fractionation remains undefined. This study evaluated the efficacy and safety of multifraction SRT with an inhomogeneous dose distribution. This retrospective study included 31 patients who underwent 33 treatments for 35 brainstem lesions using linear accelerator-based multifraction SRT (30Gy in five fractions, 35Gy in five fractions or 42Gy in 10 fractions) with an inhomogeneous dose distribution (median isodose, 51.9%). The outcomes of interest were local failure, toxicity and symptomatic failure. The median follow-up time after brainstem SRT for a lesion was 18.6months (interquartile range, 10.0-24.3months; range, 1.8-39.0months). Grade 2 toxicities were observed in two lesions, and local failure occurred in three lesions. No grade 3 or higher toxicities were observed. The 1-year local and symptomatic failure rates were 8.8 and 16.7%, respectively. Toxicity was observed in two of seven treatments with a gross tumor volume (GTV) greater than 1cc, whereas no toxicity was observed in treatments with a GTV less than 1cc. No clear association was observed between the biologically effective dose of the maximum brainstem dose and the occurrence of toxicity. Our findings indicate that multifraction SRT with an inhomogeneous dose distribution offers a favorable balance between local control and toxicity in brainstem metastases. Larger multicenter studies are needed to validate these results and determine the optimal dose fractionation.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning

PurposeArtificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches. Methods and MaterialsA vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects. ResultsOur Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms. ConclusionsAuto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy.

Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients. This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC). Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV. The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma

Rationale and ObjectivesExtraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC). Materials and MethodsA retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2,154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors. ResultsThe GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843) and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.窗体顶端 ConclusionThe GPTV3 radiomics model was superior to the GTV, GPTV6 and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics

PurposeThis study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient outcomes. Materials/MethodsAn international cohort of 117 oligometastatic castration-sensitive prostate cancer (omCSPC) patients, comprising 34 from John Hopkins Hospital (JHH) and 83 from Baskent University (BU), were treated with stereotactic ablative radiation therapy (SABR) MDT with both pre- and post-MDT PSMA-PET/CT scans acquired. PET radiomic features were analyzed from a CT-PET fusion defined gross tumor volume ((GTV) or zone 1), and a 5 mm expansion ring area outside the GTV (zone 2). A total of 1748 PET radiomic features were extracted from these zones. The six most significant features selected using the Chi2 method, along with five clinical parameters (age, Gleason score, number of total lesions, untreated lesions, and pre-MDT prostate-specific antigen (PSA)) were extracted as inputs to the models. Various machine learning models, including Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayesian, were employed for 2-year MFS prediction and tested using leave-one-out and cross-institution validation. ResultsSix radiomic features, including Total Energy, Entropy, and Standard Deviation from pre-PSMA-PET zone 1, Total Energy and Contrast from post-PSMA-PET zone 1, and Entropy from pre-PSMA-PET zone 2, along with five clinical parameters were selected for predicting 2-year MFS. In a leave-one-out test with all the patients, random forest achieved an accuracy of 80 % and an AUC of 0.82 in predicting 2-year MFS. In cross-institution validation, the model correctly predicted 2-year MFS events with an accuracy of 75 % and an AUC of 0.77 for patients from JHH, and an accuracy of 78 % and an AUC of 0.80 for BU patients, respectively. ConclusionOur study demonstrated the promise of using pre- and post-MDT PSMA-PET-based imaging biomarkers for MFS prediction for omCSPC patients.

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology

BackgroundIn this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).MethodsMultiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation.ResultsThe CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN’s trained with mpMRI and parametric clinical and the CNN’s trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28.ConclusionThe results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring.Trial registrationThe trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.

Association of increasing gross tumor volume dose with tumor volume reduction and local control in fractionated stereotactic radiosurgery for unresected brain metastases

BackgroundFractionated stereotactic radiosurgery (fSRS) is an important treatment strategy for unresected brain metastases. We previously reported that a good volumetric response 6 months after fSRS can be the first step for local control. Few studies have reported the association between gross tumor volume (GTV) dose, volumetric response, and local control in patients treated with the same number of fractions. Therefore, in this study, we aimed to investigate the GTV dose and volumetric response 6 months after fSRS in five daily fractions and identify the predictive GTV dose for local failure (LF) for unresected brain metastasis.MethodsThis retrospective study included 115 patients with 241 unresected brain metastases treated using fSRS in five daily fractions at our hospital between January 2013 and April 2022. The median prescription dose was 35 Gy (range, 30–35 Gy) in five fractions. The median follow-up time after fSRS was 16 months (range, 7–66 months).ResultsGTV D80 > 42 Gy and GTV D98 > 39 Gy were prognostic factors for over 65% volume reduction (odds ratio, 3.68, p < 0.01; odds ratio, 4.68, p < 0.01, respectively). GTV D80 > 42 Gy was also a prognostic factor for LF (hazard ratio, 0.37; p = 0.01).ConclusionsGTV D80 > 42 Gy in five fractions led to better volume reduction and local control. The goal of planning an inhomogeneous dose distribution for fSRS in brain metastases may be to increase the GTV D80 and GTV D98. Further studies on inhomogeneous dose distributions are required.

Consideration of Optimal Evaluation Metrics for Internal Gross Tumor Dose Relevant to Tumor Response in Multi-fraction Stereotactic Radiosurgery of Brain Metastasis.

Introduction In stereotactic radiosurgery (SRS) for brain metastasis (BM), the target dose inhomogeneity remains highly variable among modalities, irradiation techniques, and facilities, which can affect tumor response during and after multi-fraction SRS. Volumetric-modulated arcs (VMAs) can provide a concentrically-layered steep dose increase inside a gross tumor volume (GTV) boundarycompared to dynamic conformal arcs. This study was conducted to review the optimal evaluation method for the internal GTV doses relevant to maximal response and local control, specifically to examine the significance of the doses 2 mm and 4 mm inside the GTV boundary in VMA-based SRS. Materials and methods This was a planning study for the clinical scenario of a single BM and targeted 25 GTVs of >0.50 cc, including eight spherical models with diameters of 10-45 mm and 17 clinical BMs (GTV: 0.72-44.33 cc). SRS plans were generated for each GTV using VMA with a 5-mm leaf-width multileaf collimator and the optimization that prioritized the steepness of the dose gradient outside the GTV boundary without any internal dose constraints. The dose prescription and evaluation were based on the GTV D V-0.01 cc, a minimum dose of GTV minus 0.01 cc. Two planning systems were compared for the GTV - 2 mm and GTV - 4 mm structures that were generated by equally reducing 2 mm and 4 mm from the GTV surface. The D eIIVs, a minimum dose of the irradiated isodose volume equivalent to the GTV - 2 mm and GTV - 4 mm, were compared to other common metrics. Results The GTV - 2 mm and GTV - 4 mm volumes differed significantly between the systems. In the spherical GTVs, the irradiated isodose surfaces of GTV D 80% and D 50% corresponded to 0.4-1.6 mm (<2 mm) and 1.0-4.6 mm inside the GTV boundary, respectively. In the 25 GTVs, the GTV - 2 mm coverage with the D eIIV varied from 83.7% to 98.2% (95-98% in 68% of the cases), while the GTV coverage with the GTV - 2 mm D eIIV was 20.2-75.9%. In the 23 GTVs of ≥1.26 cc, the GTV coverage with the GTV - 4 mm D eIIVvaried from 1.9% to 55.6% (<50% in 87% of the cases). No significant difference was observed between the GTV D 50% and the GTV - 2 mm D eIIV, while the GTV - 4 mm D eIIV was significantly higher than the GTV D 50%. No significant correlations were observed between the GTV D 50% and the D eIIVs of the GTV - 2 mm and GTV - 4 mm. Conclusions The doses 2 mm and 4 mm inside a GTV have low correlations with the GTV D 50% and may be more relevant tomaximal response and local control for SRS of BM. The D eIIVinstead of the minimum dose of a fixed % coverage (e.g. D 98%) is suitable for reporting the doses 2 mm and 4 mm inside the GTV boundary in terms of avoiding the over- or under-coverage, with consideration tosubstantial variability inminus margin addition functions among planning systems. In VMA-based SRS with a steep dose gradient, the doses 2-4 mm inside a GTV decrease significantly as the GTV increases, which can attenuate the excessive dose exposure to the surrounding brain in a large BM due to the GTV shrinkage during multi-fraction SRS.

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

BackgroundThe 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.MethodsPANCRINJ 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.ResultsThe 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.ConclusionsOur 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.

Role of adaptive radiotherapy during concomitant chemoradiotherapy for limited-stage small-cell lung cancer.

Tumor shrinkage is frequently observed during conventionally fractionated chemoradiotherapy for limited-stage small-cell lung cancer (SCLC). The specific goals of this study are to evaluate the gross tumor volume (GTV) changes due to treatment-induced tumor reduction during the course of radiotherapy (RT) and to examine its potential use in adaptive radiotherapy (ART) for tumor dose escalation or normal tissue sparing in patients with SCLC. A total of 10 patients with SCLC eligible for chemoradiotherapy underwent computed tomography (CT) scan after Fractions 13 and 23 (at nominal doses of 23.4 Gy and 41.4 Gy, respectively). The GTV was delineated on the repeat CT scans, and two treatment plans were generated with or without adaptation to tumor shrinkage during RT for each patient. Dosimetric and volumetric analyses were performed. The average GTV reduction observed over 13 fractions was 58.5% (range: 13.2%-92.3%; P < 0.001) and over 23 fractions was 70% (range: 36.9%-84.5%; P < 0.001). Compared with the plan without adaptation, ART resulted in mean lung dose relative decreases of 8.7%, mean lung volume receiving ≥20 Gy relative decreases of 5%, mean lung volume receiving ≥5Gy relative decreases of 10%, mean medulla spinalis dose relative decreases of 21 cGy, mean esophagus volume receiving ≥50 Gy relative decreases of 19%, and mean heart volume receiving ≥42 Gy relative decreases of 13%. The benefits of ART were the greatest for tumor volumes ≥30 cm3 and are directly dependent on GTV reduction during treatment. ART for SCLC achieved a significant benefit in terms of organ at risk (OAR) and dose escalation.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors

PurposeThis study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors. MethodsMultiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation. ResultsEven for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %–20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities. ConclusionsA higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

The predictors of lymphopenia and its effects on survival in locally advanced esophageal squamous cell carcinoma

ABSTRACT To investigate the impact of the effective radiation dose to immune cells (EDIC) and gross tumor volume (GTV) on lymphopenia and survival in patients with locally advanced esophageal squamous cell carcinoma (LAESCC). Between January 2013 and December 2020, 272 LAESCC patients were treated with definitive radiotherapy in two institutions. Based on radiation doses to the lungs, heart, and body region scanned, EDIC was calculated as an equal uniform dose to the total blood considering blood flow and fraction effect. The radiotherapy plan was used to calculate the GTVs. Lymphopenia was graded based on the lowest lymphocyte count during RT. The overall survival (OS), progress-free survival (PFS), and local recurrence-free survival (LRFS) were analyzed statistically. The lowest lymphocyte count was significantly correlated with EDIC (r= −0.389, p < .001) and GTV (r= −0.211, p < .001). Lymphopenia, EDIC, and GTV are risk factors for patients with ESCC. In a Kaplan-Meier analysis with EDIC and GTV as stratification factors, lymphopenia was not associated with OS in the EDIC>12.9 Gy group (p = .294)and EDIC ≤ 12.9 Gy group, and it was also not associated with OS in GTV>68.8 cm3 group (p = .242) and GTV ≤ 68.8 cm3 group(p = .165). GTV and EDIC had an impact on the relationship between lymphopenia and OS in patients with LAESCC undergoing definitive RT. Poorer OS, PFS, and LRFS are correlated with lymphopenia, higher EDIC, and larger GTV.