Radiotherapy Planning CT Scan Research Articles

Radiation risk during thoracic CT scan for diagnostic and radiotherapy planning procedures in Hassan II, Hospital, Agadir Morocco

Objective: This study aims to establish diagnostic reference levels and radiation-induced risk for the diagnostic CT-scans and the radiotherapy planning CT-scans of the thorax in the regional hospital of Agadir, Morocco. 
 Material and Method: Data from two groups of patients undergoing thoracic CT-scans with either diagnostic CT-scans (G1, n=120) or Radiotherapy planning CT-scans (G2, n=120) are collected. All acquisitions were helical. DRLs is calculated for each type of thoracic CT-scan by estimating the 75% percentile of the CTDIvol and the DLP. The total cancer risk RC was calculated according to the ICRP publication 103.
 The data are statistically analyzed by SPSS Statistics V21.0. The Pearson’s rank correlation coefficient is chosen to study the relationship between the following parameters: DLP, CTDIvol, effective dose, and the cancer risk.
 Results: DRLs in terms of CTDIvol and DLP for radiotherapy planning of thorax were 19.37mGy and 851.9 mGy.cm respectively. In diagnostic CT-scans, DRLs in terms of CTDIvol for pulmonary embolism, infectious lung disease,Chronic Obstructive Pulmonary Disease (COPD) were 11.13mGy, 10.26mGy, and 7.37mGy respectively, and DRLs in terms of DLP were 417,73 mGy.cm, 451,9 mGy.cmand 317,78 mGy.cmrespectively. The cancer risk for radiotherapy planning CT-scans is ranged between 209 and 1564 with a mean value of 715 per 1 million of CT-scan. For diagnostic CT-scans, the cancer risk is ranged between 199 and 626 with a mean value of 357 per 1 million for pulmonary embolism, between 238 and 668 with a mean value of 369 per 1 million for infectious lung disease, and between 130 and 393 with a mean value of 244 per 1 million for COPD.
 Conclusion: Optimizing the doses received by patients in medical imaging, particularly CT, has become an obligation. Reviewing practices and procedures and promoting a radiation protection culture can help to better manage the radiation doses received by the public.

Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis

BackgroundPositron emission tomography–magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis.MethodsTen patients being treated with ano-rectal radiotherapy received a 18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of p≤0.05/7=0.007\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p \\le 0.05/7 = 0.007$$\\end{document}. Equivalence margins of ±3.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 3.5\\%$$\\end{document} were used.ResultsMean whole-image SUV differences were −0.02% (sCTAC) compared to −3.0% (MRAC), with larger differences in the bone regions (−0.5% to −16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients ≥0.987\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 0.987$$\\end{document}. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\max}$$\\end{document} were 1.0±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.0 \\pm 0.8\\%$$\\end{document} (± standard error, sCTAC) and -4.6±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-4.6 \\pm 0.9\\%$$\\end{document} (MRAC), and 1.0±0.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.0 \\pm 0.7\\%$$\\end{document} (sCTAC) and -4.3±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-4.3 \\pm 0.8\\%$$\\end{document} (MRAC) in SUVmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\rm mean}$$\\end{document}. The sCTAC was statistically equivalent to CTAC within a ±3.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 3.5\\%$$\\end{document} equivalence margin for SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\max}$$\\end{document} and SUVmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\rm mean}$$\\end{document} (p=0.007\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.007$$\\end{document} and p=0.002\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.002$$\\end{document}), whereas the MRAC was not (p=0.88\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.88$$\\end{document} and p=0.83\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.83$$\\end{document}).ConclusionAttenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

The Effect of Wire Versus Magnetic Seed Localization on Lumpectomy Cavity Size

PurposeTo assess whether an association exists between surgical localization technique and lumpectomy cavity size on radiotherapy planning CT scan. Methods and MaterialsA single-institution retrospective review was conducted of women undergoing breast conserving surgery with wire- or magnetic seed- guided lumpectomy followed by adjuvant radiotherapy from 2018 to 2021. Patients of a surgeon only performing one localization technique or undergoing bracketed localization were excluded. The primary outcome was lumpectomy cavity size on simulation CT. Confounding due to imbalance in patient and tumor factors was addressed with overlap weights derived from a propensity score analysis and used in a weighted multivariable analysis (MVA). Secondary outcomes included positive margins, total pathologic volume, boost delivery, and boost modality. ResultsOf 617 women who received lumpectomy during the study period, 387 were included in final analysis. Tumors of patients undergoing seed localization were more likely unifocal, assessable by ultrasound, and smaller. Seed use rates ranged from 27.7% to 70.7% per surgeon. There was no difference in positive margins (6.4 vs 5.4%, p = 0.79) or second surgeries (9.4 vs 8.1%, p = 0.79) between groups. Close margin rates were similar for DCIS (p = 0.35) and invasive carcinoma (p = 0.97). In unadjusted bivariable analyses, wire localization was associated with larger total pathology volume (p = 0.004) but localization technique showed no association with CT cavity volume (p = 0.15). After adjusting for potentially confounding variables listed in the methods, MVA failed to show an association between localization technique and either CT cavity (p=0.35), or total path volume (p = 0.08). There was no difference in indicated-boost delivery (p= 0.15) or electron boost (p= 0.14) by localization technique. ConclusionsThere was no significant difference in CT cavity size by localization technique, suggesting choice between surgical techniques does not impede radiotherapy boost delivery.

Thoracic skeletal muscle index is effective for CT-defined sarcopenia evaluation in patients with head and neck cancer

PurposeComputed tomography (CT)-defined sarcopenia, as a measurement of low skeletal muscle (SM), is a poor prognostic indicator in patients with head and neck cancer (HNC), independent of weight or nutritional status. We used SM measures at the second thoracic vertebra (T2) to determine T2-SM index (SMI) thresholds for sarcopenia, and investigate the impact of low T2-SMI on overall survival (OS), and weight loss during radiotherapy (RT).MethodsAdult patients with newly diagnosed HNC with a diagnostic PET–CT or RT planning CT scan were included. SM was analysed at T2 and a model applied to predict SM at L3. T2-SMI thresholds for sarcopenia were established with predicted measures, stratified by BMI and sex. Impact of sarcopenia and low T2-SMI on OS and weight loss during RT was investigated.ResultsA total of 361 scans were analysed (84% males, 54% oropharynx tumours). Sarcopenia was found in 49%, demonstrating worse OS (p = 0.037). T2-SMI cutoff values were: females—74 cm2/m2 [area under the curve (AUC): 0.89 (95%CI 0.80–0.98)], males (BMI < 25)—63 cm2/m2 [AUC 0.93 (95%CI 0.89–0.96)], males (BMI ≥ 25)—88cm2/m2 [AUC 0.86 (95%CI 0.78–0.93)]. No difference in OS with T2-SMI categories. Lowest T2-SMI quartile of < 63 cm2/m2 demonstrated worse OS (p = 0.017). Weight loss during RT was higher in patients; who were not sarcopenic (6.2% vs 4.9%, p = 0.023); with higher T2-SMI (6.3% vs 4.9%, p = 0.014) and; in the highest quartiles (3.6% vs 5.7% vs 7.2%, p < 0.001).ConclusionsThese T2-SMI thresholds are effective in assessing CT-defined sarcopenia in HNC. Further assessment of clinical application is warranted.

Protocol: Can coronary artery calcium score identified on thoracic planning CT scans be used and actioned to identify cancer survivors at high risk of cardiac events: A feasibility study in cancer survivors undergoing radiotherapy in Australia

IntroductionA coronary artery calcium (CAC) CT scan can identify calcified plaque and predict risk of future cardiac events. Cancer survivors undergoing thoracic radiotherapy routinely undergo a planning CT scan, which...

Does coronary artery calcium score have an impact on overall survival for locally advanced non-small cell lung cancer treated with definitive radiotherapy

Background and purposeCoronary artery calcium score (CACs) is an excellent marker for survival in non-cancer patients, but its role in locally advanced non-small cell lung cancer (LA-NSCLC) patients remains uncertain. In this study, we hypothesize that CACs is a prognostic marker for survival in a competing risk analysis in LA-NSCLC patients treated with definitive radiotherapy. Materials and methodsWe included 644 patients with LA-NSCLC treated in 2014-2015 in Denmark. Baseline patient characteristics were derived from the Danish Lung Cancer Registry. Radiotherapy planning CT scans were used for manual CACs measurements, and the patients were divided into four groups, CACs 0, 1-99, 100-399, and ≥400. A multivariable Cox model utilizing bootstrapping for cross-validation modeled overall survival (OS). ResultsThe median follow-up time was seven years, and the median OS was 26 months (95% CI 24-29). Within each CAC group 0, 1-99, 100-399, and ≥400 were 172, 182, 143, and 147 patients, respectively. In the univariable analysis, the survival decreased with increasing CACs. However, after adjustment for age, PS, radiotherapy dose, and logarithmic GTV, CACs did not have a statistically significant impact on OS with hazard ratios of 1.04 (95% CI 0.85-1.28), 1.11 (95%CI 0.89-1.43), and 1.16 (95%CI 0.92-1.47) for CACs 1-99, CACs 100-399 and ≥400, respectively. Elevated CACs was observed in 73 % of the patients suggesting a high risk of cardiac comorbidity before radiotherapy. ConclusionCACs did not add prognostic information to our population's classical risk factors, such as tumor volume, performance status, and age; the lung cancer has the highest priority despite the risk of baseline cardiac comorbidity.

Association of cardiac calcium burden with overall survival after radiotherapy for non-small cell lung cancer.

Coronary calcifications are associated with coronary artery disease in patients undergoing radiotherapy (RT) for non-small cell lung cancer (NSCLC). We quantified calcifications in the coronary arteries and aorta and investigated their relationship with overall survival (OS) in patients treated with definitive RT (Def-RT) or post-operative RT (PORT). We analyzed 263 NSCLC patients treated from 2004 to 2017. Calcium burden was ascertained with a Hounsfield unit (HU) cutoff of>130 in addition to a deep learning (DL) plaque estimator. The HU cutoff volumes were defined for coronary arteries (PlaqueCoro) and coronary arteries and aorta combined (PlaqueCoro+Ao), while the DL estimator ranged from 0 (no plaque) to 3 (high plaque). Patient and treatment characteristics were explored for association with OS. The median PlaqueCoro and PlaqueCoro+Ao was 0.75cm3 and 0.87cm3 in the Def-RT group and 0.03cm3 and 0.52cm3 in the PORT group. The median DL estimator was 2 in both cohorts. In Def-RT, large PlaqueCoro (HR:1.11 (95%CI:1.04-1.19); p=0.008), and PlaqueCoro+Ao (HR:1.06 (95%CI:1.02-1.11); p=0.03), and poor Karnofsky Performance Status (HR: 0.97 (95%CI: 0.94-0.99); p=0.03) were associated with worse OS. No relationship was identified between the plaque volumes and OS in PORT, or between the DL plaque estimator and OS in either Def-RT or PORT. Coronary artery calcification assessed from RT planning CT scans was significantly associated with OS in patients who underwent Def-RT for NSCLC. This HU thresholding method can be straightforwardly implemented such that the role of calcifications can be further explored.

Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC

Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.

Significant Incidental Findings Unrelated to the Primary Cancer on CT Simulation Treatment Planning Scans

<h3>Purpose/Objective(s)</h3> Incidental tumor findings on radiographic imaging are rare and often do not manifest symptoms such as a lump or associated pain. These tumors may represent major clinically relevant findings that need additional imaging and testing. Although some studies published metastatic disease of the primary cancer, we are not aware of publications in the U.S. focusing solely on major, significant findings, and unrelated to the primary disease during radiotherapy planning. In this paper, we introduce several cases where CT planning images revealed significant clinical findings unrelated to the primary cancer that required further investigation. All patients with these major findings were followed-up for diagnostic imaging, examination, and proper referral. <h3>Materials/Methods</h3> To simulate real-life situation when CT treatment planning is performed in a radiation oncology department, the radiation oncologist reviewed a total of 115 patients with CT treatment planning that was performed during the 2-year period of 2017-2018. These CTs were performed during routine radiotherapy planning for curative purposes: brain tumors (n = 10 or 9%), head and neck cancer (n = 15 or 13%), lung cancer (n = 30 or 26%), breast cancer (n = 35 or 30%), prostate cancer (n = 25 or 22%). <h3>Results</h3> We report incidental but significant findings on radiotherapy planning CT scans unrelated to the primary cancer. A total of 4 abnormal findings (3 neoplasms and 1 benign) were discovered from a total of 115 CT scans done for radiotherapy planning. The 3 neoplasms were: hepatocellular carcinoma, thyroid cancer, and large adrenal adenoma. The one benign finding was extensive eventration of the left hemi-diaphragm. <h3>Conclusion</h3> CT planning scans are routinely performed separately in the radiation oncology department for proper treatment mapping of cancer patients. These CT images are usually stored in the RO department server and not in radiology PACS system. At the present time, it is not known how many percent of radiation oncologists routinely check CT scans for incidental findings. The 4 case reports stated in this article demonstrate that although rare, there are malignancies/abnormalities unrelated to the primary cancer that could be missed in the CT planning process. We therefore recommend that radiation oncologists take charge of this issue and implement a method of proper interpretation of CT simulation images by either the radiation oncologists responsible for the case, or designated diagnostic radiologists (if such arrangement exists), to ensure that incidental findings are not missed. In any radiation oncology (RO) department, a routine QA (quality assurance) checklist often includes: consultation note, verification of pathology, consent form, verification of dosimetry, verification of physics report etc., we believe that proper interpretation of CT simulation images for incidental findings should be part of the QA checklist in a modern RO department

A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.

To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.

The use of dose tracking software in audit of radiotherapy planning CT doses

Radiotherapy planning (RTP) CT scans are not subject to the same national diagnostic reference levels (DRLs) as diagnostic CT scans as their intended use is different. Recommended DRLs have not been implemented at national levels leading to significant variance in reported dose indices across radiotherapy departments. This study utilises dose tracking software, GE DosewatchTM, to analyse RTP protocol dose indices (CTDIvol, DLP and scan length) for Breast, Gynaecological, Head & Neck, Lung 4DCT, Lung 3D, and Prostate protocols.

Hippocampal sparing radiotherapy in adults with primary brain tumours: a comparative planning and dosimetric study using IMPT, IMRT and 3DCRT.

Abstract Introduction We assessed the feasibility of hippocampal sparing in adults with primary brain tumours using Intensity Modulated Proton Therapy (IMPT) and compared this with Intensity Modulated Radiotherapy (IMRT) and 3D-Conformal Radiotherapy (3DCRT). Methods and Materials 20 patients were identified, and each patient underwent a radiotherapy planning CT scan and 2 MRI scans. A pre-operative diagnostic MRI scan was fused with the planning CT and used for target delineation and a dedicated 3T MRI scan at the time of planning was fused with the CT for hippocampus delineation. 3 hippocampal sparing plans were generated for each patient with specific prescriptions (54Gy/30 fractions, 60Gy/30 fractions and 59.4Gy/33 fractions) using IMPT, IMRT and 3DCRT. Hippocampal sparing was defined as median dose to contralateral hippocampus ≤25Gy without compromising target coverage and organ at risk dose constraints. Results Hippocampal sparing was achieved in 19 patients (95%) with IMPT, 16 patients (80%) with IMRT and 13 patients (65%) with 3DCRT. The largest median hippocampal dose reduction was seen with IMPT, with a mean median hippocampal dose of 4.8Gy (range: 0.0Gy-24.9Gy), 14.6Gy (range: 1.9Gy-21.7Gy), and 16.2Gy (range: 2.3Gy-25.0Gy) for IMPT, IMRT and 3DCRT respectively. Hippocampal sparing IMPT failed in one case with the largest tumour volume (650cc) where 2/3 of the hippocampus overlapped the target volume. Conclusion IMPT as compared to IMRT and 3DCRT plans showed a trend towards significant and effective hippocampal sparing in adult patients with primary brain tumours. We are currently evaluating this in a larger patient cohort and comparing IMPT with VMAT.

Bragatston study protocol: a multicentre cohort study on automated quantification of cardiovascular calcifications on radiotherapy planning CT scans for cardiovascular risk prediction in patients with breast cancer

IntroductionCardiovascular disease (CVD) is an important cause of death in breast cancer survivors. Some breast cancer treatments including anthracyclines, trastuzumab and radiotherapy can increase the risk of CVD, especially for...

Prognostic impact of tumor growth velocity in head and neck squamous cell carcinoma treated by radiotherapy: A pilot study.

When a patient is seen with a newly diagnosed oropharyngeal squamous cell carcinoma, it remains unclear to the treating physicians how fast the tumor growth rate is. From patients with oropharynx squamous cell carcinoma treated by radiotherapy, the investigators selected comparable diagnostic CT-scan (DiCT) and radiotherapy planning CT-scan (RtCT). Tumor and pathological lymph node volumes were measured in order to calculate tumor progression. From the selection of 19 patients, the mean absolute tumor progression rate was 0.23 ± 0.2 cm3 /d and mean relative progression rate was 1.84 ± 1.64%/d. Mean tumor doubling time is 286 days (range 7-1282 days), demonstrating a wide range of tumor growth pattern. Significant tumor progression (>20%) between DiCT and RtCT was shown in 73% of patients, and 53% of the patients were seen a tumor progression of >50% within a mean waiting time of 42.1 days. Kaplan-Meier curves showed a non-significative link between fast progression tumors (>1%/d) and higher risk of recurrence (HR: 2.2; P = .23). Tumor progression can be assessed based on DiCT and RtCT. Treatment delay should be avoided at all cost. Different growth patterns were evidenced. For the fast-growing tumors subgroup, pejorative clinical outcomes were suggested. Prospective studies are needed to confirm a link between fast-growing tumors and higher risk for recurrence.

Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.

Objective:This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests, FEV1 (forced expiratory volume in 1 s) and TLCO (transfer factor of carbon monoxide).Methods:An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it.Results:Density and entropy scores were compared between a cohort of 29 fit patients (defined as FEV1 and TLCO above 50 % predicted value) and 32 unfit patients (FEV1 or TLCO below 50% predicted). Mean and median density and median entropy were significantly different between fit and unfit patients (p = 0.005, 0.0008 and 0.0418 respectively; two-sided Mann–Whitney test).Conclusion:Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging.Advances in knowledge:This study shows that a novel assessment can generate further data from standard CT imaging. These data could be combined with existing studies to form a multiorgan patient fitness assessment from a single CT scan.

A Radiomic Prognostic Index Refines Risk Models for Distant Metastasis in HPV-Related Oropharyngeal Cancer

Distant metastasis (DM) is the main cause of treatment failure in human papillomavirus (HPV)-related oropharyngeal cancers (OPC). However, predicting DM remains challenging. We hypothesize that quantitative imaging (i.e. radiomic) features may help identify primary tumor features associated with higher risk of DM. We retrieved radiotherapy planning CT scans for 280 UICC/AJCC 8th ed. stage I-III p16-positive OPC patients treated with definitive radiotherapy or chemoradiotherapy at a single institution between 2005 and 2010. Gross tumor volumes (GTVs) were previously contoured by each treating radiation oncologist. A radiomic prognostic index (RPI) was derived based on four validated prognostic radiomic features from tumor intensity, shape, texture, and wavelet feature groups. RPIs were extracted from each GTV based on published algorithms. We used univariate and multivariate analysis with Cox proportional hazards models to identify predictors of DM. Feature selection for multivariate models was performed using recursive partitioning analysis (RPA) and validated. Receiver-operating characteristic (ROC) curves were used to compare models. Median follow-up was five years. There were a total of 34 DM events. Factors associated with increased risk of DM included UICC/AJCC 8th ed. stage III (T4 or N3) (73/280; p<0.001), large GTV (140/280; p=0.048), and high risk RPI (25/280; p<0.001). High risk RPI remained significant (p<0.001) on multivariate analysis. RPA confirmed RPI as a key predictor segregating p16-positive OPCs into low (n=255) and high (n=25) risks of DM with 3-year distant control rates of 91% (0.88-0.95) and 50% (0.32-0.78) respectively. Area under the ROC curve for the RPI-based DM model was 0.72 (0.62-0.82) compared with 0.65 (0.56-0.74) for the conventional stage-based DM model. In subgroup analyses, the RPI consistently stratified patients for DM risk, particularly for those cohorts with greater risk, such as UICC/AJCC 8th ed. stage III cancers. The four-feature RPI can be used alone or in conjunction with other clinical or treatment characteristics to classify HPV-related OPC patients by DM risk. The RPI may therefore allow for greater precision in selection of patients for systemic therapies for HPV-related OPC.

Prospective Evaluation of the Relationship Between Mechanical Stability and Response to Palliative Radiotherapy for Symptomatic Spinal Metastases

A substantial number of patients with spinal metastases experience no treatment effect from palliative radiotherapy. Mechanical spinal instability, due to metastatic disease, could be associated with failed pain control following radiotherapy. This study investigates the relationship between the degree of spinal instability, as defined by the Spinal Instability Neoplastic Score (SINS), and response to radiotherapy in patients with symptomatic spinal metastases in a multi-institutional cohort. The SINS of 155 patients with painful thoracic, lumbar, or lumbosacral metastases from two tertiary hospitals was calculated using images from radiotherapy planning CT scans. Patient-reported pain response, available for 124 patients, was prospectively assessed. Pain response was categorized, according to international guidelines, as complete, partial, indeterminate, or progression of pain. The association between SINS and pain response was estimated by multivariable logistic regression analysis, correcting for predetermined clinical variables. Of the 124 patients, 16 patients experienced a complete response and 65 patients experienced a partial response. Spinal Instability Neoplastic Score was associated with a complete pain response (adjusted odds-radio [ORadj] 0.78; 95% confidence interval [CI] 0.62-0.98), but not with an overall pain response (ORadj 0.94; 95% CI 0.81-1.10). A lower SINS, indicating spinal stability, is associated with a complete pain response to radiotherapy. This supports the hypothesis that pain resulting from mechanical spinal instability responds less well to radiotherapy compared with pain from local tumor activity. No association could be determined between SINS and an overall pain response, which might indicate that this referral tool is not yet optimal for prediction of treatment outcome. Patients with stable painful spinal metastases, as indicated by a Spinal Instability Neoplastic Score (SINS) of 6 or lower, can effectively be treated with palliative external beam radiotherapy. The majority of patients with (impending) spinal instability, as indicated by a SINS score of 7 or higher, will achieve a (partial) response after palliative radiotherapy; however, some patients might require surgical intervention. Therefore, it is recommended to refer patients with a SINS score of 7 or higher to a spine surgeon to evaluate the need for surgical intervention.

Natural progression and clinical significance of incidentally detected pulmonary nodules in radiotherapy planning CT scans of breast cancer patients: a retrospective cohort

AbstractAimIndeterminate pulmonary nodules incidentally detected during radiological imaging completed for radiotherapy planning always creates dilemma for the oncologist. The purpose of this study is to evaluate the clinical significance of pulmonary nodules incidentally detected in patients undergoing locoregional radiotherapy for breast cancer and present a retrospective analysis of the natural progression of such nodules.MethodsA retrospective review of computed tomography scans of breast cancer patients who underwent radiotherapy over a period of 3 years to screen out patients with indeterminate lung nodules was undertaken. This was correlated with the patient and tumour characteristics and the status of the disease at last follow-up.ResultsOf the 132 patients reviewed 28 had indeterminate lung nodules. Of the 28 patients, four had progressive lung nodules on follow-up. Subgroup analyses did not show any significant correlation.Discussion and conclusionOne fifth of patients may present with incidentally detected lung nodules. Multiple nodules, ER negative status and locally advanced breast cancer may point to a higher risk of these nodules progressing to metastatic cancer. There is no indication to stop locoregional therapy in the presence of indeterminate nodules, but close follow-up of high-risk group is recommended.

OC-0374: Use of IV contrast media in pre-treatment radiotherapy planning CT scans: A UK study

OC-0374: Use of IV contrast media in pre-treatment radiotherapy planning CT scans: A UK study

Abstract P3-12-22: Reproducibility of automated coronary artery calcification scoring on radiotherapy treatment planning computed tomography scans of breast cancer patients

Abstract Introduction: Presence of coronary artery calcifications (CAC) is a major independent risk factor for cardiovascular (CV) disease. CAC can be visualized on CT scans. Most breast cancer patients planned for radiotherapy (RT) receive planning CT scans. These scans may provide a reliable estimate of a patients' CV risk. This study evaluated the feasibility and reproducibility of an automated algorithm for CAC scoring on RT planning CT scans of breast cancer patients. Methods: This study was conducted within the Utrecht cohort for Multiple BReast cancer intErvention studies and Long-term evaLuAtion (UMBRELLA), and includes 562 breast cancer patients undergoing RT at University Medical Center Utrecht. Planning CT scans were performed using a 16-slice scanner (16 x 0.75 mm collimation, 3 mm thickness, 120 kVp, with or without breath hold (BH), without ECG synchronization). CAC were automatically scored using an algorithm developed with chest CT scans that considers lesions &amp;gt;130 Hounsfield units as CAC. CAC were identified using a supervised pattern recognition based on texture, size, and spatial features. To test validity of automated CAC scoring, manually scoring by an expert was performed in all scans with CAC (n = 80) and a random sample of scans without CAC (n = 83). Interscan reproducibility of automated CAC scoring was assessed in patients having two scans (n = 295). All scans with CAC score ≥ 1000 were manually checked and corrected if appropriate. Agatston calcification scores were analyzed continuously and categorically (0, 1-10, 11-100, 101-400, &amp;gt;400). Agreement and reliability for categories were determined with proportional agreement (%) and linearly weighted kappa. Reliability of Agatston scores were assessed with Intraclass correlation coefficients (ICC). Results: Of 562 patients, 129 (23%) patients had CAC scores &amp;gt; 0 with a mean of 93 (standard deviation: 166). Four patients had CAC scores ≥ 1000, which were erroneous and corrected. Of the 163 CT scans scored manually and automatically, 58 (36%) were performed with BH. Proportion of agreement was 79% (95% Confidence Interval (CI): 0.72-0.85) for all 163 scans: 88% (0.76-0.95) for 58 scans with BH and 74% (0.65-0.82) for 105 scans without. Proportion of agreement beyond chance was 0.80 (95% CI: 0.74-0.87) for all scans: 0.86 (0.77-0.96) with BH and 0.77 (0.684-0.853) without. Agatston score ICC was 0.86 (95% CI: 0.82-0.90) for all scans: 0.95 (0.91-0.97) with BH and 0.67 (0.55-0.76) without. For the interscan reproducibility (n = 295), the majority of patients (81%) had one scan with BH and one scan without. Proportion of agreement was 84% (95% CI: 0.79-0.88) and reliability was 0.61 (95% CI: 0.50-0.72). Agatston score ICC was 0.75 (95% CI: 0.69-0.80). Conclusion: Automated CAC scoring on RT planning CT scans of breast cancer patients is feasible. Agreement with manually scored scans is high and higher in CT scans performed with BH. Interscan reproducibility is fair. Automated CAC scores ≥ 1000 should to be manually checked and corrected if necessary. Automated CAC scoring on RT planning CT scans of breast cancer patients is available for all patients undergoing RT, and can provide information on CV risk at no additional cost. Citation Format: Gernaat SAM, de Vos BD, Isgum I, Rijnberg N, Bijlsma RM, Takx RAP, Pignol JP, Leiner T, Grobbee DE, van der Graaf Y, van den Bongard DHJG, Verkooijen HM. Reproducibility of automated coronary artery calcification scoring on radiotherapy treatment planning computed tomography scans of breast cancer patients. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-12-22.