Tumor Centroid Research Articles

Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer

BackgroundSince it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients.MethodsThis study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters.ResultsOf 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS.ConclusionNHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.

A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor’s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT). Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT’s robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT. Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality. Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability of a priori 5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients.

The normalized distances from the hot spot of radiotracer uptake (SUVmax) to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) have recently been suggested as novel PET features reflecting tumor aggressiveness. These biomarkers characterizing the shift of SUVmax toward the lesion edge during tumor progression have been shown to be prognostic factors in breast and non-small cell lung cancer (NSCLC) patients. We assessed the impact of imaging parameters on NHOC and NHOP, their complementarity to conventional PET features, and their prognostic value for advanced-NSCLC patients. Methods: This retrospective study investigated baseline [18F]FDG PET scans: cohort 1 included 99 NSCLC patients with no treatment-related inclusion criteria (robustness study); cohort 2 included 244 NSCLC patients (survival analysis) treated with targeted therapy (93), immunotherapy (63), or immunochemotherapy (88). Although 98% of patients had metastases, radiomic features including SUVs were extracted from the primary tumor only. NHOCs and NHOPs were computed using 2 approaches: the normalized distance from the localization of SUVmax or SUVpeak to the tumor centroid or perimeter. Bland-Altman analyses were performed to investigate the impact of both spatial resolution (comparing PET images with and without gaussian postfiltering) and image sampling (comparing 2 voxel sizes) on feature values. The correlation of NHOCs and NHOPs with other features was studied using Spearman correlation coefficients (r). The ability of NHOCs and NHOPs to predict overall survival (OS) was estimated using the Kaplan-Meier method. Results: In cohort 1, NHOC and NHOP features were more robust to image filtering and to resampling than were SUVs. The correlations were weak between NHOCs and NHOPs (r ≤ 0.45) and between NHOCs or NHOPs and any other radiomic features (r ≤ 0.60). In cohort 2, the patients with short OS demonstrated higher NHOCs and lower NHOPs than those with long OS. NHOCs significantly distinguished 2 survival profiles in patients treated with immunotherapy (log-rank test, P < 0.01), whereas NHOPs stratified patients regarding OS in the targeted therapy (P = 0.02) and immunotherapy (P < 0.01) subcohorts. Conclusion: Our findings suggest that even in advanced NSCLC patients, NHOC and NHOP features pertaining to the primary tumor have prognostic potential. Moreover, these features appeared to be robust with respect to imaging protocol parameters and complementary to other radiomic features and are now available in LIFEx software to be independently tested by others.

A feasibility trial of skin surface motion-gated stereotactic body radiotherapy for treatment of upper abdominal or lower thoracic targets using a novel O-ring gantry

Background and purposeA novel O-ring gantry can deliver stereotactic body radiation therapy (SBRT) with artificial intelligence-facilitated, CT-guided online plan adaptation. It gates mobile targets by optically monitoring skin surface motion. However, this gating solution has not been clinically validated. We conducted a trial to evaluate the feasibility of optical skin surface-guided gating for patients with mobile upper abdominal or lower thoracic malignancies treated with SBRT on this platform (NCT05030454). Materials and methodsTen patients who were prescribed SBRT to a thoracic or abdominal target and were capable of breath-hold for at least 17 s enrolled. They received SBRT in five fractions with breath-hold technique and optical skin surface motion monitored-gating with a ± 2 mm tolerance. Online plan adaptation was left to the discretion of the daily treating physician. The primary endpoint was defined as successful completion of > 75 % of attempted fractions. Exploratory endpoints included local control and acute grade ≥ 3 toxicity rates after three months. For adapted fractions the contouring, planning, quality assurance, and treatment delivery times were recorded. ResultsForty-seven of 51 SBRT fractions (92 %) were successfully gated at breath-hold by optical skin surface motion monitoring. The tumor centroid position during breath-hold varied by a mean of approximately 2 mm. Sixty-three percent of fractions were adapted online with a median total treatment time of 78.5 min. After three months no local recurrences or acute grade ≥ 3 toxicities were observed. ConclusionsSBRT treatment to mobile targets with surface-monitored gating on a novel O-ring gantry was prospectively validated.

Theranostic Nanoparticle Uptake in Patient Brain Tumors as Quantified by MP2RAGE T1 Mapping

Modern radiation therapy techniques provide effective treatments for solid tumors, but there remain challenges with delivering high doses to elusive tumors without causing toxicity to surrounding normal tissue. Pre-clinical trials have demonstrated the theranostic properties of a recently developed gadolinium-based nanoparticle (Gd-NP). The first in-human clinical trial was conducted to assess the safety and dose tolerance of intravenous Gd-NPs in combination with whole brain radiotherapy and showed favorable results, including a significant correlation between tumor uptake and therapeutic response. The second, double-blinded, in-human clinical trial is underway in the US and aims to evaluate if brain-directed stereotactic radiation in conjunction with NPs will improve local tumor control compared to radiation alone. The current work investigates uptake patterns in brain tumors of 23 patients as quantified by magnetization prepared 2 rapid gradient echo (MP2RAGE) T1 mapping. A phantom containing eight vials of NP-saline solutions at varying concentrations was created to examine the relationship between NP concentration and longitudinal relaxation (T1, in seconds). This relationship is known as relaxivity and is dependent on the contrast agent, field strength and T1 mapping sequence. A 3T MAGNETOM Vida scanner and MP2RAGE sequence were used to image the phantom and MP2RAGE T1 maps were calculated using Bloch equations (QMRLab software). Relaxivity was determined and applied to 23 patient T1 maps (pre- and post- Gd-NP administration) to calculate uptake on an individual tumor basis. Theranostic NP uptake was calculated for every voxel in each of 129 brain metastases and examined for patterns in quantity and distribution. Average individual tumor uptake ranged from 0.02-0.12 mg/ml, where average overall uptake was equal to 0.05 mg/ml. A relationship between tumor diameter and mean NP concentration was observed and best represented by a power-based curve (R2 = 0.92). In contrast, patients with suspected placebo administration appeared to have no uptake and therefore no relationship with tumor diameter. The distribution of NP concentration within the tumor was also examined; on average, linear uptake profiles through tumor centroids (ant-post, left-right) demonstrated roughly gaussian patterns of uptake with lower concentrations at the tumor edges and higher concentrations at the tumor center. This pattern indicates robust tumor penetration and may have implications for amplifying radiation dose to hypoxic tumors. Gd-NP uptake in brain metastases can be quantified using MP2RAGE T1 mapping. Uptake was determined for each voxel in each tumor volume, where a gaussian pattern of spatial concentrations was observed. This analysis procedure will be applied to the full data set, when available, to evaluate the impact of NP uptake (in conjunction with radiation therapy) within individual patients and individual tumors.

X-ray source arrays for volumetric imaging during radiotherapy treatment

This work presents a novel hardware configuration for radiotherapy systems to enable fast 3D X-ray imaging before and during treatment delivery. Standard external beam radiotherapy linear accelerators (linacs) have a single X-ray source and detector located at ± 90° from the treatment beam respectively. The entire system can be rotated around the patient acquiring multiple 2D X-ray images to create a 3D cone-beam Computed Tomography (CBCT) image before treatment delivery to ensure the tumour and surrounding organs align with the treatment plan. Scanning with a single source is slow relative to patient respiration or breath holds and cannot be performed during treatment delivery, limiting treatment delivery accuracy in the presence of patient motion and excluding some patients from concentrated treatment plans that would be otherwise expected to have improved outcomes. This simulation study investigated whether recent advances in carbon nanotube (CNT) field emission source arrays, high frame rate (60 Hz) flat panel detectors and compressed sensing reconstruction algorithms could circumvent imaging limitations of current linacs. We investigated a novel hardware configuration incorporating source arrays and high frame rate detectors into an otherwise standard linac. We investigated four potential pre-treatment scan protocols that could be achieved in a 17 s breath hold or 2–10 1 s breath holds. Finally, we demonstrated for the first time volumetric X-ray imaging during treatment delivery by using source arrays, high frame rate detectors and compressed sensing. Image quality was assessed quantitatively over the CBCT geometric field of view as well as across each axis through the tumour centroid. Our results demonstrate that source array imaging enables larger volumes to be imaged with acquisitions as short as 1 s albeit with reduced image quality arising from lower photon flux and shorter imaging arcs.

Defining subventricular zone involvement to predict the survival of patients in isocitrate dehydrogenase-wild type glioblastoma: validation in a prospective registry.

The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94-0.99; p = .011) and definition 2 (HR: 0.96, 0.94-0.98, p < .001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94-0.99, p = .008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3years (AUC: 0.735-0.738 vs. 0.687-0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. •Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. •Longer SVD was independently associated with longer overall survival. •Adding SVD improved time-dependent specificity for survival model in a prospective registry.

MP58-06 EVALUATION OF A TOUCH-BASED IMAGE GUIDANCE SYSTEM FOR ROBOTIC PARTIAL NEPHRECTOMY

You have accessJournal of UrologyCME1 Apr 2023MP58-06 EVALUATION OF A TOUCH-BASED IMAGE GUIDANCE SYSTEM FOR ROBOTIC PARTIAL NEPHRECTOMY Shaan Setia, Piper Canon, James Ferguson, Nicholas Kavoussi, Robert Webster, and Duke Herrell Shaan SetiaShaan Setia More articles by this author , Piper CanonPiper Canon More articles by this author , James FergusonJames Ferguson More articles by this author , Nicholas KavoussiNicholas Kavoussi More articles by this author , Robert WebsterRobert Webster More articles by this author , and Duke HerrellDuke Herrell More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003311.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Image-guidance during robotic partial nephrectomy (rPN) employs a 3D model of renal anatomy to allow for sub-surface identification of structures. Most image guidance surgery (IGS) systems involve manual registration, which is inaccurate and requires a separate individual to align the model intraoperatively. We sought to evaluate an automated, touch-based 3D IGS system and its accuracy compared to expert surgeons. METHODS: We identified patients with renal masses with planned rPN. Preop CT scans were used to generate virtual patient-specific 3D anatomical kidney models. Our IGS system was deployed during 6 rPNs by two primary surgeons. Registration was achieved by aligning a touch-based point cloud of the kidney surface tracing (using the robotic tool tip) to the models. The primary surgeon used the tool tip to estimate the location of the renal artery, vein, tumor centroid and intersection of the mass with parenchyma. After registration, a secondary surgeon (blinded to preop imaging) used the tool tip to record the location of these structures with the IGS system. The ground-truth, intraoperative locations of the target structures were recorded after dissection was complete. Target registration errors (TRE) with and without IGS were compared. RESULTS: Computed registration was performed successfully across 6 patients (mean axial diameter 3.0 cm, mean nephrometry score 6.5, Figure 1). After surface tracing, registration on average took 100 seconds. Median overall TRE for the secondary surgeon using the IGS compared to the primary surgeon was 9.8 vs 8.6 mm, respectively (p=0.25). TRE of localization was less for the lesion intersection with kidney (3.7 vs 2.2 mm, p=0.43) compared to the renal artery (14.1 vs 18.7 mm, p=0.61) and vein (14.4 vs 9.6 mm, p=0.06) (Figure 2). CONCLUSIONS: We report an automated, touch-based IGS system for rPN. The system is as accurate as an expert surgeon in target structure identification, though accuracy of registration for rigid structures was better than that of hilar vessels. Further improvements are needed to accurately model tissue deformation especially for hilar anatomy. Source of Funding: NIH R01-EB023717Software interface support from Intuitive © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e797 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Shaan Setia More articles by this author Piper Canon More articles by this author James Ferguson More articles by this author Nicholas Kavoussi More articles by this author Robert Webster More articles by this author Duke Herrell More articles by this author Expand All Advertisement PDF downloadLoading ...

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy

Background and purposeMagnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future. Materials and methodsModels were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour. ResultsThe online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models. ConclusionLSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.

Surface Imaging-Guided Adaptive Radiotherapy (ART): Imaging Protocol and Verification for Inter- and Intra-Fractional Motion Managements

<h3>Purpose/Objective(s)</h3> CBCT-guided adaptive radiotherapy (CT-ART) has proven to account for inter-fractional anatomic changes. However, no commercial solution for intra-fractional respiratory motion management existed in CT-ART. Therefore, we investigated intra-fractional motion-managed treatment strategies in CT-ART (or Image-guided RT: IGRT) using optical surface imaging combined with <i>in-situ</i> CBCTs. <h3>Materials/Methods</h3> Eight of ten cancer patients with mobile lower lung or upper abdominal malignancies have completed CT-ART (or IGRT) using Ethos radiotherapy in an IRB-approved clinical trial (phase I). Four of the eight patients underwent additional MR simulations, including cine MRIs, to estimate the tumor/abdominal skin motion correlation. In CT-ART, a pre-configured gating window (±2 mm in vertical position) on surface imaging was used for manually triggering intra-fractional CBCT and treatment beam irradiation in breath-hold (six patients for exhalation and two patients for inhalation). A donut-shaped imaging-surrogate on patient's abdomen was used to localize Region of Interest (ROI) of the abdomen on CBCTs. Two inter-fractional CBCTs at exhalation and inhalation in each fraction were acquired to verify the tumor/imaging-surrogate motions range. Intra-fractional CBCTs were used to quantify the residual motion of the tumor/imaging-surrogate within the pre-configured gating window. Tumor motion was measured by tracking the position of a tumor centroid. <h3>Results</h3> The pre-configured gating window on surface imaging was confirmed based on the correlation of the tumor and abdominal skin motions using cine MRIs. Thirty-six pairs of inter-fractional CBCTs and 100 intra-fractional CBCTs from 36 fractions were analyzed (13 fractions for IGRT and 23 for ART). In the motion range study from the inter-fractional CBCTs, the values of mean ± std were 17.3 ± 12.0 mm in the SI direction for the tumors and 16.6 ± 7.9 mm in the AP direction for the imaging-surrogate, respectively. The residual motion of the imaging-surrogate from intra-fractional CBCTs was 2.5 ± 2.1 mm for SI, 2.3 ± 2.0 mm for RL, and 2.1 ± 2.3 mm for AP directions. The residual motion of the tumors was 2.4 ± 3.1 mm for SI, 2.3 ± 2.3 mm for RL, and 2.2 ± 2.0 mm for AP directions. Trends in the residual motion during the course of treatment fractions and over time through delivery were stable. Inter-fractional residual motions of the tumors were 2.3 ± 0.8 mm for SI, 2.3 ± 0.6 mm for RL, and 2.2 ± 0.7 mm for AP directions. Intra-fractional residual motions of the tumors were 2.4 ± 0.9 mm for SI, 2.4 ± 0.7 mm for RL, and 3.2 ± 1.3 mm for AP. <h3>Conclusion</h3> We demonstrated the inter- and intra-fractional motion-managed treatment strategies in CBCT-guided ART using surface imaging. Since the residual motion of the tumors was comparable to that of the imaging-surrogate within clinical PTV margins (5 mm) but a bit larger than the pre-configured gating window, additional caution is needed with intra-fractional motion management in CT-ART using the optical surface imaging.

Deep learning-based synthetization of real-time in-treatment 4D images using surface motion and pretreatment images: A proof-of-concept study.

To develop a deep learning model that maps body surface motion to internal anatomy deformation, which is potentially applicable to dose-free real-time 4D virtual image-guided radiotherapy based on skin surface data. Body contours were segmented out of 4DCT images. Deformable image registration algorithm was used to register the end-of-exhalation (EOE) phase to other phases. Deformation vector field was dimension-reduced to the first two principal components (PCs). A deep learning model was trained to predict the two PC scores of each phase from surface displacement. The instant deformation field can then be reconstructed, warping EOE image to obtain real-time CT image. This approach was validated on 4D XCAT phantom, the public DIR-Lab, and 4D-Lung dataset respectively, with and without simulated noise. Validation accuracy of the tumor centroid trajectory was observed as 0.04 ± 0.02mm on XCAT phantom. For the DIR-Lab dataset, 300 landmarks were annotated on the end-of-inhalation (EOI) images of each patient, and the mean displacements between their predicted and reference positions were below 2mm for all studied cases. For the 4D-Lung dataset, the average dice coefficients ± std between predicted and reference tumor contours at EOI phase were 0.835 ± 0.092 for all studied cases. A deep learning-based approach was proposed and validated to predict internal anatomy deformation from the surface motion, which is potentially applicable to on-line target navigation for accurate radiotherapy based on real-time 4D skin surface data and pretreatment images.

Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy

Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.

Tumor deformation correction for an image guidance system in breast conserving surgery.

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

Parameterizing the Effects of Tumor Shape in Magnetic Nanoparticle Thermotherapy Through a Computational Approach

Abstract In this work, effects of tumor shape on magnetic nanoparticle hyperthermia (MNPH) are investigated and evaluated using four categories (spherical, oblate, prolate, and egg-shape) of tumor models having different morphologies. These tumors have equal volume; however, due to the differences in their shapes, they have different surface areas. The shape of tumors is quantified in terms of shape factor (ζ). Simulations for MNPH are done on the physical model constituting tumor tissue enclosed within the healthy tissue. Magnetic hyperthermia is applied (frequency 150 kHz, and magnetic field amplitude 20.5 kA/m) to all tumor models, for 1 h, after injection of magnetic nanoparticles (MNPs) at the respective tumor centroids. The distribution of MNPs after injection is considered Gaussian. The governing model (Pennes' bioheat model) of heat transfer in biological media is solved with the finite volume-immersed boundary (FV-IB) method to simulate MNPH. Therapeutic effects are calculated using the Arrhenius tissue damage model, cumulative equivalent minutes at 43 °C (CEM 43), and heterogeneity in temperature profiles of the tumors. Results show that the therapeutic effects of MNPH depend significantly on the shape of a tumor. Tumors with higher shape factors receive less therapeutic effects in comparison to the tumors having lower shape factors. An empirical thermal damage model is also developed to assess the MNPH efficacy in real complex-shaped tumors.

Topography and clinical features of iris melanoma

BackgroundTo characterise the topographical and clinical features of primary iris melanoma and to visualise the patterns of tumour extent in the iris.MethodsClinical characteristics of iris melanomas were analysed, and data on their size, shape, and location were converted into a database of two-dimensional iris charts by means of computer-drawing software. The geometric centre of each tumour was entered into corresponding sectors of the chart. The extent of the melanomas was computationally visualised by merging the iris drawings and displaying the number of overlapping tumours on colour-coded iris maps.ResultsTwenty-nine patients (18 females and 11 males) with a mean age of 52 years met the inclusion criteria. The mean largest tumour diameter was 6.1 mm (range, 1.8–11.0 mm). Five tumours (17%) involved the pupillary margin, 10 (34%) involved the iris root, and 10 (34%) involved both sites. The hemispheric location of the tumour centroid was superior in 3 eyes (11%) and inferior in 25 (89%) (p < 0.0001), and the distribution between the temporal and nasal hemispheres was 17 (61%) and 11 (39%), respectively (p = 0.26). In females, the iris melanomas were located more temporally (p = 0.02) and had more often originated from a pre-existing naevus (p = 0.03), than in males. There was also shift towards more temporally located melanomas in younger patients.ConclusionsThe lower temporal iris quadrant is the preferential area of melanoma occurrence and growth. Iris melanoma tends to be more temporally located in females, who compared with males also have a higher proportion of melanomas arising from a pre-existing naevus.

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.

To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6±1.8 in the T2/T1-w MRI, to -4.4±2.4, 6.6±3.0, and 9.6±3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65±0.10 in the original T2/T1-w 4D-MRI, to 0.76±0.14, 0.77±0.12, and 0.86±0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2±1.3, 0.3±0.7, and 0.5±0.5mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72±0.33, 0.62±0.54, and 0.74±0.43mm in the superior-inferior (SI) direction, and 0.59±0.36, 0.51±0.30, and 0.50±0.24mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5mm in SI, AP, and left-right (LR) directions. We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

Understanding Global Brain Network Alterations in Glioma Patients.

Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

Textual fiducial detection in breast conserving surgery for a near-real time image guidance system.

Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast cancer surgery provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent information leads to difficulty in ensuring complete tumor resection. Here we propose a novel image guidance platform that utilizes character-based fiducials for easy detection and small fiducial points for precise and accurate localization. Our system is work-flow friendly, and near-real time with use of stereo cameras for surface acquisition. Using simple image processing techniques, the proposed technique can localize fiducials and character labels, providing updates without relying on video history. Character based fiducial labels can be recognized and used to determine correspondence between left and right images in a pair of stereo cameras, and frame to frame in a sequence of images during a procedure. Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized. The stereo camera system can determine surface points with accuracy below 2mm when compared to optically tracked stylus points. These surface points are incorporated to a four-panel guidance display that includes preoperative supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.