| 1. |
- Ceberg, Sofie, et al.
(författare)
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RapidArc treatment verification in 3D using polymer gel dosimetry and Monte Carlo simulation.
- 2010
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 1361-6560 .- 0031-9155. ; 55:17, s. 4885-4898
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to verify the advanced inhomogeneous dose distribution produced by a volumetric arc therapy technique (RapidArc) using 3D gel measurements and Monte Carlo (MC) simulations. The TPS (treatment planning system)-calculated dose distribution was compared with gel measurements and MC simulations, thus investigating any discrepancy between the planned dose delivery and the actual delivery. Additionally, the reproducibility of the delivery was investigated using repeated gel measurements. A prostate treatment plan was delivered to a 1.3 liter nPAG gel phantom using one single arc rotation and a target dose of 3.3 Gy. Magnetic resonance imaging of the gel was carried out using a 1.5 T scanner. The MC dose distributions were calculated using the VIMC-Arc code. The relative absorbed dose differences were calculated voxel-by-voxel, within the volume enclosed by the 90% isodose surface (VOI(90)), for the TPS versus gel and TPS versus MC. The differences between the verification methods, MC versus gel, and between two repeated gel measurements were investigated in the same way. For all volume comparisons, the mean value was within 1% and the standard deviation of the differences was within 2.5% (1SD). A 3D gamma analysis between the dose matrices were carried out using gamma criteria 3%/3 mm and 5%/5 mm (% dose difference and mm distance to agreement) within the volume enclosed by the 50% isodose surface (VOI(50)) and the 90% isodose surface (VOI(90)), respectively. All comparisons resulted in very high pass rates. More than 95% of the TPS points were within 3%/3 mm of both the gel measurement and MC simulation, both inside VOI(50) and VOI(90). Additionally, the repeated gel measurements showed excellent consistency, indicating reproducible delivery. Using MC simulations and gel measurements, this verification study successfully demonstrated that the RapidArc plan was both accurately calculated and delivered as planned.
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| 2. |
- Colvill, Emma, et al.
(författare)
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A dosimetric comparison of real-time adaptive and non-adaptive radiotherapy : A multi-institutional study encompassing robotic, gimbaled, multileaf collimator and couch tracking
- 2016
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140. ; 119:1, s. 159-165
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Tidskriftsartikel (refereegranskat)abstract
- Purpose A study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presence of patient-measured tumor motion. Methods and materials Ten institutions with robotic(2), gimbaled(2), MLC(4) or couch tracking(2) used common materials including CT and structure sets, motion traces and planning protocols to create a lung and a prostate plan. For each motion trace, the plan was delivered twice to a moving dosimeter; with and without real-time adaptation. Each measurement was compared to a static measurement and the percentage of failed points for γ-tests recorded. Results For all lung traces all measurement sets show improved dose accuracy with a mean 2%/2 mm γ-fail rate of 1.6% with adaptation and 15.2% without adaptation (p < 0.001). For all prostate the mean 2%/2 mm γ-fail rate was 1.4% with adaptation and 17.3% without adaptation (p < 0.001). The difference between the four systems was small with an average 2%/2 mm γ-fail rate of <3% for all systems with adaptation for lung and prostate. Conclusions The investigated systems all accounted for realistic tumor motion accurately and performed to a similar high standard, with real-time adaptation significantly outperforming non-adaptive delivery methods.
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| 3. |
- Frennered, Anna, et al.
(författare)
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Patterns of pathologic lymph nodes in anal cancer : a PET-CT-based analysis with implications for radiotherapy treatment volumes
- 2021
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: This study investigates the patterns of PET-positive lymph nodes (LNs) in anal cancer. The aim was to provide information that could inform future anal cancer radiotherapy contouring guidelines. Methods: The baseline [18F]-FDG PET-CTs of 190 consecutive anal cancer patients were retrospectively assessed. LNs with a Deauville score (DS) of ≥3 were defined as PET-positive. Each PET-positive LN was allocated to a LN region and a LN sub-region; they were then mapped on a standard anatomy reference CT. The association between primary tumor localization and PET-positive LNs in different regions were analyzed. Results: PET-positive LNs (n = 412) were identified in 103 of 190 patients (54%). Compared to anal canal tumors with extension into the rectum, anal canal tumors with perianal extension more often had inguinal (P < 0.001) and less often perirectal (P < 0.001) and internal iliac (P < 0.001) PET-positive LNs. Forty-two patients had PET-positive LNs confined to a solitary region, corresponding to first echelon nodes. The most common solitary LN region was inguinal (25 of 42; 60%) followed by perirectal (26%), internal iliac (10%), and external iliac (2%). No PET-positive LNs were identified in the ischiorectal fossa or in the inguinal area located posterolateral to deep vessels. Skip metastases above the bottom of the sacroiliac joint were quite rare. Most external iliac PET-positive LNs were located posterior to the external iliac vein; only one was located in the lateral external iliac sub-region. Conclusions: The results support some specific modifications to the elective clinical target volume (CTV) in anal cancer. These changes would lead to reduced volumes of normal tissue being irradiated, which could contribute to a reduction in radiation side-effects.
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| 4. |
- Jamtheim Gustafsson, Christian, et al.
(författare)
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Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy
- 2021
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Ingår i: Journal of Applied Clinical Medical Physics. - : John Wiley & Sons. - 1526-9914. ; 22:12, s. 51-63
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Tidskriftsartikel (refereegranskat)abstract
- Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures.Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1–3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic.A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data.Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic
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| 5. |
- Lempart, Michael, et al.
(författare)
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A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs
- 2021
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Ingår i: Radiotherapy and Oncology. - 1879-0887. ; 161:Suppl 1, s. 1417-1418
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel, large bowel, rectum, and bladder).
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| 6. |
- Lempart, Michael, et al.
(författare)
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Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy
- 2023
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Ingår i: Journal of Applied Clinical Medical Physics. - 1526-9914. ; 24:9
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL-based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with coherent or mixed guidelines was investigated. A supervised 3D squeeze-and-excite SENet-154 model was trained to classify two bowel cavity delineation guidelines. In addition, a pelvis CT dataset with manual delineations from prostate cancer patients (n = 1854) was used where data with an alternative guideline for femoral heads, rectum, and bladder were generated using commercial software. The model was evaluated on internal (n = 200) and external test data (n = 99). By using mixed, compared to coherent, delineation guideline training data mean DICE score decreased 3% units, mean Hausdorff distance (95%) increased 5 mm and mean surface distance (MSD) increased 1 mm. The classification of bowel cavity test data achieved 99.8% unweighted classification accuracy, 99.9% macro average precision, 97.2% macro average recall, and 98.5% macro average F1. Corresponding metrics for the pelvis internal test data were all 99% or above and for the external pelvis test data they were 96.3%, 96.6%, 93.3%, and 94.6%. Impaired segmentation performance was observed for training data with mixed guidelines. The DL delineation classification models achieved excellent results on internal and external test data. This can facilitate automated guideline-specific data extraction while avoiding the need for consistent and correct structure labels.
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| 7. |
- Lempart, Michael, et al.
(författare)
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Pelvic U-Net : multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network
- 2022
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. Methods: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1–4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. Results: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. Conclusions: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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| 8. |
- Nilsson, Martin P., et al.
(författare)
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Patterns of recurrence in anal cancer : A detailed analysis
- 2020
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Anal cancer is a rare disease, which might be the reason for the "one size fits all" approach still used for radiotherapy target contouring. To refine and individualize future guidelines, detailed and contemporary pattern of recurrence studies are needed. Methods: Consecutive anal cancer patients, all treated with curative intent intensity-modulated radiotherapy (IMRT), were retrospectively studied (n = 170). Data was extracted from medical records and radiological images. Radiotherapy planning CT's and treatment plans were reviewed, and recurrences were mapped and categorized according to radiation dose. Results: The mean dose to the primary tumor was 59.0 Gy. With a median follow-up of 50 months (range 14-117 months), 5-year anal cancer specific survival was 86.1%. Only 1 of 20 local recurrences was located outside the high dose (CTVT) volume. More patients experienced a distant recurrence (n = 34; 20.0%) than a locoregional recurrence (n = 24; 14.1%). Seven patients (4.2%) had a common iliac and/or para-aortic (CI/PA) recurrence. External iliac lymph node involvement (P = 0.04), and metastases in ≥3 inguinal or pelvic lymph node regions (P = 0.02) were associated with a 15-18% risk of CI/PA recurrence. Following chemoradiotherapy, 6 patients with recurrent or primary metastatic CI/PA lymph nodes were free of recurrence at last follow-up. The overall rate of ano-inguinal lymphatic drainage (AILD) recurrence was 2 of 170 (1.2%), and among patients with inguinal metastases at initial diagnosis it was 2 of 65 (3.1%). Conclusions: We conclude that other measures than increased margins around the primary tumor are needed to improve local control. Furthermore, metastatic CI/PA lymph nodes, either at initial diagnosis or in the recurrent setting, should be considered potentially curable. Patients with certain patterns of metastatic pelvic lymph nodes might be at an increased risk of harboring tumor cells also in the CI/PA lymph nodes.
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| 9. |
- Nilsson, Martin P., et al.
(författare)
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Sarcopenia and dosimetric parameters in relation to treatment-related leukopenia and survival in anal cancer
- 2021
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Treatment-related white blood cell (WBC) toxicity has been associated with an inferior prognosis in different malignancies, including anal cancer. The aim of the present study was to investigate predictors of WBC grade ≥ 3 (G3+) toxicity during chemoradiotherapy (CRT) of anal cancer. Methods: Consecutive patients with locally advanced (T2 ≥ 4 cm—T4 or N+) anal cancer scheduled for two cycles of concomitant 5-fluorouracil and mitomycin C chemotherapy were selected from an institutional database (n = 106). All received intensity modulated radiotherapy (IMRT; mean dose primary tumor 59.5 Gy; mean dose elective lymph nodes 45.1 Gy). Clinical data were extracted from medical records. The highest-grade WBC toxicity was recorded according to CTCAE version 5.0. Pelvic bone marrow (PBM) was retrospectively contoured and dose-volume histograms were generated. The planning CT was used to measure sarcopenia. Dosimetric, anthropometric, and clinical variables were tested for associations with WBC G3+ toxicity using the Mann–Whitney test and logistic regression. Cox proportional hazard regression was used to assess predictors for overall survival (OS) and anal cancer specific survival (ACSS). Results: WBC G3+ was seen in 50.9% of the patients, and 38.7% were sarcopenic. None of the dosimetric parameters showed an association with WBC G3+ toxicity. The most significant predictor of WBC G3+ toxicity was sarcopenia (adjusted OR 4.0; P = 0.002). Sarcopenia was also associated with an inferior OS (adjusted HR 3.9; P = 0.01), but not ACSS (P = 0.07). Sensitivity analysis did not suggest that the inferior prognosis for sarcopenic patients was a consequence of reduced doses of chemotherapy or a prolonged radiation treatment time. Patients who experienced WBC G3+ toxicity had an inferior OS and ACSS, even after adjustment for sarcopenia. Conclusions: Sarcopenia was associated with increased risks of both WBC G3+ toxicity and death following CRT for locally advanced anal cancer. In this study, radiation dose to PBM was not associated with WBC G3+ toxicity. However, PBM was not used as an organ at risk for radiotherapy planning purposes and doses to PBM were high, which may have obscured any dose–response relationships.
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