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A block matching-based registration algorithm for localization of locally advanced lung tumors.

ABSTRACT: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy.Small (1 cm(3)), nonoverlapping image subvolumes ("blocks") were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4-7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration.Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%-100%) in the initial bony alignment to 91% ± 8% (range: 56%-100%;p < 0.001). Left-right, anterior-posterior, and superior-inferior systematic BDE were 3.2, 2.4, and 4.4 mm, respectively, with random BDE of 2.4, 2.1, and 2.7 mm. Margins required to include both localization and delineation uncertainties ranged from 5.0 to 11.7 mm, an average of 40% less than required for bony alignment.BMR is a promising approach for automatic lung tumor localization. Further evaluation is warranted to assess the accuracy and robustness of BMR against other potential localization strategies.

SUBMITTER: Robertson SP

PROVIDER: S-EPMC3978354 | biostudies-other | 2014 Apr

REPOSITORIES: biostudies-other

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Project description:PURPOSE:Locally advanced non-small cell lung cancer (NSCLC) patients may experience dramatic changes in anatomy during radiotherapy and could benefit from adaptive radiotherapy (ART). Deformable image registration (DIR) is necessary to accurately accumulate dose during plan adaptation, but current algorithms perform poorly in the presence of large geometric changes, namely atelectasis resolution. The goal of this work was to develop a DIR framework, named Consistent Anatomy in Lung Parametric imagE Registration (CALIPER), to handle large geometric changes in the thorax. METHODS:Registrations were performed on pairs of baseline and mid-treatment CT datasets of NSCLC patients presenting with atelectasis at the start of treatment. Pairs were classified based on atelectasis volume change as either full, partial, or no resolution. The evaluated registration algorithms consisted of several combinations of a hybrid intensity- and feature-based similarity cost function to investigate the ability to simultaneously match healthy lung parenchyma and adjacent atelectasis. These components of the cost function included a mass-preserving intensity cost in the lung parenchyma, use of filters to enhance vascular structures in the lung parenchyma, manually delineated lung lobes as labels, and several intensity cost functions to model atelectasis change. Registration error was quantified with landmark-based target registration error and post-registration alignment of atelectatic lobes. RESULTS:The registrations using both lobe labels and vasculature enhancement in addition to intensity of the CT images were found to have the highest accuracy. Of these registrations, the mean (SD) of mean landmark error across patients was 2.50 (1.16) mm, 2.80 (0.70) mm, and 2.04 (0.13) mm for no change, partial resolution, and full atelectasis resolution, respectively. The mean (SD) atelectatic lobe Dice similarity coefficient was 0.91 (0.08), 0.90 (0.08), and 0.89 (0.04), respectively, for the same groups. Registration accuracy was comparable to healthy lung registrations of current state-of-the-art algorithms reported in literature. CONCLUSIONS:The CALIPER algorithm developed in this work achieves accurate image registration for challenging cases involving large geometric and topological changes in NSCLC patients, a requirement for enabling ART in this patient group.

Project description:ImportanceAlthough outcome of surgical resection of liver metastases from pancreatic neuroendocrine tumors (PNETs) has been extensively studied, little is known about surgery for locally advanced PNETs; it was listed recently by the European neuroendocrine tumor society as a major unmet need.ObjectiveTo evaluate the outcome of patients who underwent surgery for locally aggressive PNETs.Design, setting, and participantsThis retrospective single-center case series reviewed consecutive patients who underwent resection of T3/T4 PNETs at a single academic institution. Data collection occurred from 2003 to 2018. Data analysis was performed in August 2019.Main outcomes and measuresDisease-free survival (primary outcome) and overall mortality (secondary outcome) were assessed with Kaplan-Meier analysis. Recurrence risk (secondary outcome, defined as identification of tumor recurrence on imaging) was assessed with Cox proportional hazard models adjusting for covariates.ResultsIn this case series, 99 patients with locally advanced nondistant metastatic PNET (56 men [57%]) with a mean (SEM) age of 57.0 (1.4) years and a mean (SEM) follow-up of 5.3 (0.1) years underwent surgically aggressive resections. Of those, 4 patients (4%) underwent preoperative neoadjuvant treatment (including peptide receptor radionuclide therapy and chemotherapy); 18 patients (18%) underwent pancreaticoduodenectomy, 68 patients (69%) had distal or subtotal pancreatic resection, 10 patients (10%) had total resection, and 3 patients (3%) had other pancreatic procedures. Additional organ resection was required in 86 patients (87%): spleen (71 patients [71%]), major blood vessel (17 patients [17%]), bowel (2 patients [2%]), stomach (4 patients [4%]), and kidney (2 patients [2%]). Five-year disease-free survival was 61% (61 patients) and 5-year overall survival was 91% (91 patients). Of those living, 75 patients (76%) had an Eastern Cooperative Oncology Group score of less than or equal to 1 at last followup. Lymph node involvement (HR, 7.66; 95% CI, 2.78-21.12; P < .001), additional organ resected (HR, 6.15; 95% CI, 1.61-23.55; P = .008), and male sex (HR, 3.77; 95% CI, 1.68-8.97; P = .003) were associated with increased risk of recurrence. Functional tumors had a lower risk of recurrence (HR, 0.23; CI, 0.06-0.89; P = .03). Required resection of blood vessels was not associated with a significant increase recurrence risk.Conclusions and relevanceIn this case series, positive lymph node involvement and resection of organs with tumor involvement were associated with an increased recurrence risk. These subgroups may require adjuvant systemic treatment. These findings suggest that patients with locally advanced PNETs who undergo surgical resection have excellent disease-free and overall survival.

Project description:The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients. Five stage IIIA/IIIB NSCLC patient cohorts were retrospectively collected. Patients were treated either with radiochemotherapy (RCT): RCT1* (n = 107), RCT2 (n = 95), RCT3 (n = 37) or with surgery combined with radiotherapy or chemotherapy: S1* (n = 135), S2 (n = 55). Based on a deformable image registration (MIM Vista, 6.9.2.), an in-house developed software transferred each primary tumor to the CT scan of a reference patient while maintaining the original tumor shape. A frequency-weighted cumulative status map was created for both exploratory cohorts (indicated with an asterisk), where the spatial extent of the tumor was uni-labeled with 2 years OS. For the exploratory cohorts, a permutation test with random assignment of patient status was performed to identify regions with statistically significant worse OS, referred to as decreased survival areas (DSA). The minimal Euclidean distance between primary tumor to DSA was extracted from the independent cohorts (negative distance in case of overlap). To account for the tumor volume, the distance was scaled with the radius of the volume-equivalent sphere. For the S1 cohort, DSA were located at the right main bronchus whereas for the RCT1 cohort they further extended in cranio-caudal direction. In the independent cohorts, the model based on distance to DSA achieved performance: AUCRCT2 [95% CI] = 0.67 [0.55-0.78] and AUCRCT3 = 0.59 [0.39-0.79] for RCT patients, but showed bad performance for surgery cohort (AUCS2 = 0.52 [0.30-0.74]). Shorter distance to DSA was associated with worse outcome (p = 0.0074). In conclusion, this explanatory analysis quantifies the value of primary tumor location for OS prediction based on cumulative status maps. Shorter distance of primary tumor to a high-risk region was associated with worse prognosis in the RCT cohort.

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A block matching-based registration algorithm for localization of locally advanced lung tumors.

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