Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

PURPOSE: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients. METHODS AND MATERIALS: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches. The VoxelMorph method was investigated using anatomical scans (VMorph_Sc) or label images (VMorph_Msk), or the combination of both (VMorph_Sc_Msk). Accumulated doses were compared with the planning dose. RESULTS: The DSC ranges, averaged for prostate, rectum and bladder, were 0.60-0.71, 0.67-0.79, 0.93-0.98, and 0.89-0.96 for the FFD, VMorph_Sc, VMorph_Msk, and VMorph_Sc_Msk methods, respectively. When including both anatomical and label images, VoxelMorph estimated more complex deformations resulting in heterogeneous determinant of Jacobian and higher percentage of deformation vector field (DVF) folding (up to a mean value of 1.90% in the prostate). Large differences were observed between DL-based methods regarding estimation of the accumulated dose, showing systematic overdosage and underdosage of the bladder and rectum, respectively. The difference between planned mean dose and accumulated mean dose with VMorph_Sc_Msk reached a median value of +6.3 Gy for the bladder and -5.1 Gy for the rectum. CONCLUSION: The estimation of the deformations using DL-based approach is feasible for male pelvic anatomy but requires the inclusion of anatomical contours to improve organ correspondence. High variability in the estimation of the accumulated dose depending on the deformable strategy suggests further investigation of DL-based techniques before clinical deployment.

A first-in-human study investigating biodistribution, safety and recommended dose of a new radiolabeled MAb targeting FZD10 in metastatic synovial sarcoma patients.

BACKGROUND: Synovial Sarcomas (SS) are rare tumors occurring predominantly in adolescent and young adults with a dismal prognosis in advanced phases. We report a first-in-human phase I of monoclonal antibody (OTSA-101) targeting FZD10, overexpressed in most SS but not present in normal tissues, labelled with radioisotopes and used as a molecular vehicle to specifically deliver radiation to FZD10 expressing SS lesions. METHODS: Patients with progressive advanced SS were included. In the first step of this trial, OTSA-101 in vivo bio-distribution and lesions uptake were evaluated by repeated whole body planar and SPECT-CT scintigraphies from H1 till H144 after IV injection of 187 MBq of 111In-OTSA-101. A 2D dosimetry study also evaluated the liver absorbed dose when using 90Y-OTSA-101. In the second step, those patients with significant tumor uptake were randomized between 370 MBq (Arm A) and 1110 MBq (Arm B) of 90Y-OTSA-101 for radionuclide therapy. RESULTS: From January 2012 to June 2015, 20 pts. (median age 43 years [21-67]) with advanced SS were enrolled. Even though 111In-OTSA-101 liver uptake appeared to be intense, estimated absorbed liver dose was less than 20 Gy for each patient. Tracer intensity was greater than mediastinum in 10 patients consistent with sufficient tumor uptake to proceed to treatment with 90Y-OTSA-101: 8 were randomized (Arm A: 3 patients and Arm B: 5 patients) and 2 were not randomized due to worsening PS. The most common Grade ≥ 3 AEs were reversible hematological disorders, which were more frequent in Arm B. No objective response was observed. Best response was stable disease in 3/8 patients lasting up to 21 weeks for 1 patient. CONCLUSIONS: Radioimmunotherapy targeting FZD10 is feasible in SS patients as all patients presented at least one lesion with 111In-OTSA-101 uptake. Tumor uptake was heterogeneous but sufficient to select 50% of pts. for 90Y-OTSA-101 treatment. The recommended activity for further clinical investigations is 1110 MBq of 90Y-OTSA-101. However, because of hematological toxicity, less energetic particle emitter radioisopotes such as Lutetium 177 may be a better option to wider the therapeutic index. TRIAL REGISTRATION: The study was registered on the NCT01469975 website with a registration code NCT01469975 on November the third, 2011.

An image-based method to synchronize cone-beam CT and optical surface tracking.

The integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.

A review of 177Lu dosimetry workflows: how to reduce the imaging workloads?

177 Lu radiopharmaceutical therapy is a standardized systemic treatment, with a typical dose of 7.4 GBq per injection, but its response varies from patient to patient. Dosimetry provides the opportunity to personalize treatment, but it requires multiple post-injection images to monitor the radiopharmaceutical's biodistribution over time. This imposes an additional imaging burden on centers with limited resources. This review explores methods to lessen this burden by optimizing acquisition types and minimizing the number and duration of imaging sessions. After summarizing the different steps of dosimetry and providing examples of dosimetric workflows for 177 Lu -DOTATATE and 177 Lu -PSMA, we examine dosimetric workflows based on a reduced number of acquisitions, or even just one. We provide a non-exhaustive description of simplified methods and their assumptions, as well as their limitations. Next, we detail the specificities of each normal tissue and tumors, before reviewing dose-response relationships in the literature. In conclusion, we will discuss the current limitations of dosimetric workflows and propose avenues for improvement.

"Mid-P strategy" versus "internal target volume strategy in locally advanced non small cell lung cancer: Clinical results from the randomized non-comparative phase II study Mid-P.

BACKGROUND: Locally advanced non-small cell lung cancer (LA-NSCLC) reported poor 5-year survival rates with frequent local or regional recurrences. Personalized RT may contribute to improve control and clinical outcome. We investigated efficacy and tolerance of "Mid-position" (Mid-P) strategy versus the conventional Internal Target Volume (ITV) strategy in LA-NSCLC patients treated by definitive conformal radiotherapy. METHODS: This prospective non-comparative randomized monocentric phase II trial included adult patients with non-resected, non-metastatic, non-previously irradiated proven LA-NSCLC treated with definitive normo-fractionated conformal radiotherapy (+/- chemotherapy). Allocated patients (randomisation 2:1) were treated using Mid-P or ITV strategy. A Fleming single-stage design (1-sided α = 0.1, 80 % power, P0 = 30 %, P1 = 50 %) planned enrolment of 36 patients in the Mid-P group. The ITV group ensured the absence of selection bias. The primary outcome was 1-year progression-free- survival (1y-PFS) rate. RESULTS: Among 54 eligible patients included from September 2012 to May 2018, 51 patients were analyzed (Mid-P: N = 34; ITV: 17). The 1y-PFS was 38 % (1-sided 95 %CI 25 %-not reached) with Mid-P strategy, and 47 % (95 %CI [27 %-not reached[) with ITV. Loco-regional failure as first event mainly occurred within radiation-field regardless the strategy. Acute and middle-term radiation toxicities were observed with both strategies. CONCLUSION: Local control and survival remain poor using the Mid-P strategy in this prospective randomized non-comparative monocentric study investigating Mid-P strategy versus ITV strategy in LA-NSCLC. Since the Mid-P strategy is not integrated into routine software, and perceived as a time-consuming method, Mid-P strategy cannot be recommended in LA-NSCLCC treated by definitive normo-fractionated conformal radiotherapy outside clinical trials.

Anatomy-based correction of kidney PVE on [Formula: see text] SPECT images.

BACKGROUND: In peptide receptor radionuclide therapy (PRRT), accurate quantification of kidney activity on post-treatment SPECT images paves the way for patient-specific treatment. Due to the limited spatial resolution of SPECT images, the partial volume effect (PVE) is a significant source of quantitative bias. In this study, we aimed to evaluate the performance and robustness of anatomy-based partial volume correction (PVC) algorithms to recover the accurate activity concentration of realistic kidney geometries on [Formula: see text]Lu SPECT images recorded under clinical conditions. METHODS: Based on the CT scan data from patients, three sets of fillable kidneys with surface-to-volume (S:V) ratios ranging from 1.5 to 2.8 cm-1, were 3D printed and attached in a IEC phantom. Quantitative [Formula: see text]Lu SPECT/CT acquisitions were performed on a GE Discovery NM CT 870 DR camera for the three modified IEC phantoms and for 6 different Target-To-Background ratios (TBRs: 2, 4, 6, 8, 10, 12). Two region-based (GTM and Labbé) and five voxel-based (GTM + MTC, Labbé + MTC, GTM + RBV, Labbé + RBV and IY) methods were evaluated with this data set. Additionally, the robustness of PVC methods to Point Spread Function (PSF) discrepancies, registration mismatches and background heterogeneity was evaluated. RESULTS: Without PVC, the average kidney RCs across all TBRs ranged from 0.66 ± 0.05 (smallest kidney) to 0.80 ± 0.03 (largest kidney). For a TBR of 12, all anatomy-based method were able to recover the kidneys activity concentration with an error < 6%. All methods result in a comparable decline in RC restoration with decreasing TBR. The Labbé method was the most robust against PSF and registration mismatches but was also the most sensitive to background heterogeneity. Among the voxel-based methods, MTC images were less uniform than RBV and IY images at the outer edge of high uptake areas (kidneys and spheres). CONCLUSION: Anatomy-based PVE correction allows for accurate SPECT quantification of the [Formula: see text]Lu activity concentration with realistic kidney geometries. Combined with recent progress in deep-learning algorithms for automatic anatomic segmentation of whole-body CT, these methods could be of particular interest for a fully automated OAR dosimetry pipeline with PVE correction.

PET digitization chain for Monte Carlo simulation in GATE.

OBJECTIVE: We introduce a versatile methodology for the accurate modelling of PET imaging systems via Monte Carlo simulations, using the Geant4 application for tomographic emission (GATE) platform. Accurate Monte Carlo modelling involves the incorporation of a complete analytical signal processing chain, called the digitizer in GATE, to emulate the different count rates encountered in actual PET systems. Approach: The proposed approach consists of two steps: 1) modelling the digitizer to replicate the detection chain of real systems, covering all available parameters, whether publicly accessible or supplied by manufacturers; 2) estimating the remaining parameters, i.e. background noise level, detection efficiency, and pile-up, using optimisation techniques based on experimental single and prompt event rates. We show that this two-step optimisation reproduces the other experimental count rates (true, scatter, and random), without the need for additional adjustments. This method has been applied and validated with experimental data derived from the NEMA count losses test for three state-of-the-art SiPM-based TOF-PET systems: Philips Vereos, Siemens Biograph Vision 600 and GE Discovery MI 4-ring. Main results: The results show good agreement between experiments and simulations for the three PET systems, with absolute relative discrepancies below 3~\%, 6~\%, 6~\%, 7~\% and 12~\% for prompt, random, true, scatter and noise equivalent count rates, respectively, within the 0-10~kBq.mL$^{-1}$ activity concentration range typically observed in whole-body \isotope[18]{F} scans. Significance: Overall, the proposed digitizer optimisation method was shown to be effective in reproducing count rates and NECR for three of the latest generation SiPM-based TOF-PET imaging systems. The proposed methodology could be applied to other PET scanners.

Ultra-hypofractionated prostate cancer radiotherapy: Dosimetric impact of real-time intrafraction prostate motion and daily anatomical changes.

PURPOSE: To retrospectively assess the differences between planned and delivered dose during ultra-hypofractionated (UHF) prostate cancer treatments, by evaluating the dosimetric impact of daily anatomical variations alone, and in combination with prostate intrafraction motion. METHODS: Prostate intrafraction motion was recorded with a transperineal ultrasound probe in 15 patients treated by UHF radiotherapy (36.25 Gy/5 fractions). The dosimetric objective was to cover 99 % of the clinical target volume with the 100 % prescription isodose line. After treatment, planning CT (pCT) images were deformably registered onto daily Cone Beam CT to generate pseudo-CT for dose accumulation (accumulated CT, aCT). The interplay effect was accounted by synchronizing prostatic shifts and beam geometry. Finally, the shifted dose maps were accumulated (moved-accumulated CT, maCT). RESULTS: No significant change in daily CTV volumes was observed. Conversely, CTV V100% was 98.2 ± 0.8 % and 94.7 ± 2.6 % on aCT and maCT, respectively, compared with 99.5 ± 0.2 % on pCT (p < 0.0001). Bladder volume was smaller than planned in 76 % of fractions and D5cc was 33.8 ± 3.2 Gy and 34.4 ± 3.4 Gy on aCT (p = 0.02) and maCT (p = 0.01) compared with the pCT (36.0 ± 1.1 Gy). The rectum was smaller than planned in 50.3 % of fractions, but the dosimetric differences were not statistically significant, except for D1cc, found smaller on the maCT (33.2 ± 3.2 Gy, p = 0.02) compared with the pCT (35.3 ± 0.7 Gy). CONCLUSIONS: Anatomical variations and prostate movements had more important dosimetric impact than anatomical variations alone, although, in some cases, the two phenomena compensated. Therefore, an efficient IGRT protocol is required for treatment implementation to reduce setup errors and control intrafraction motion.

Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy - A Proof of Concept.

PURPOSE: The dose deposited outside of the treatment field during external photon beam radiation therapy treatment, also known as out-of-field dose, is the subject of extensive study as it may be associated with a higher risk of developing a second cancer and could have deleterious effects on the immune system that compromise the efficiency of combined radio-immunotherapy treatments. Out-of-field dose estimation tools developed today in research, including Monte Carlo simulations and analytical methods, are not suited to the requirements of clinical implementation because of their lack of versatility and their cumbersome application. We propose a proof of concept based on deep learning for out-of-field dose map estimation that addresses these limitations. METHODS AND MATERIALS: For this purpose, a 3D U-Net, considering as inputs the in-field dose, as computed by the treatment planning system, and the patient's anatomy, was trained to predict out-of-field dose maps. The cohort used for learning and performance evaluation included 3151 pediatric patients from the FCCSS database, treated in 5 clinical centers, whose whole-body dose maps were previously estimated with an empirical analytical method. The test set, composed of 433 patients, was split into 5 subdata sets, each containing patients treated with devices unseen during the training phase. Root mean square deviation evaluated only on nonzero voxels located in the out-of-field areas was computed as performance metric. RESULTS: Root mean square deviations of 0.28 and 0.41 cGy/Gy were obtained for the training and validation data sets, respectively. Values of 0.27, 0.26, 0.28, 0.30, and 0.45 cGy/Gy were achieved for the 6 MV linear accelerator, 16 MV linear accelerator, Alcyon cobalt irradiator, Mobiletron cobalt irradiator, and betatron device test sets, respectively. CONCLUSIONS: This proof-of-concept approach using a convolutional neural network has demonstrated unprecedented generalizability for this task, although it remains limited, and brings us closer to an implementation compatible with clinical routine.

An efficient numerical tool for dose deposition prediction applied to synchrotron medical imaging and radiation therapy.

Medical imaging and radiation therapy are widely used synchrotron-based techniques which have one thing in common: a significant dose delivery to typically biological samples. Among the ways to provide the experimenters with image guidance techniques indicating optimization strategies, Monte Carlo simulation has become the gold standard for accurately predicting radiation dose levels under specific irradiation conditions. A highly important hampering factor of this method is, however, its slow statistical convergence. A track length estimator (TLE) module has been coded and implemented for the first time in the open-source Monte Carlo code GATE/Geant4. Results obtained with the module and the procedures used to validate them are presented. A database of energy-absorption coefficients was also generated, which is used by the TLE calculations and is now also included in GATE/Geant4. The validation was carried out by comparing the TLE-simulated doses with experimental data in a synchrotron radiation computed tomography experiment. The TLE technique shows good agreement versus both experimental measurements and the results of a classical Monte Carlo simulation. Compared with the latter, it is possible to reach a pre-defined statistical uncertainty in about two to three orders of magnitude less time for complex geometries without loss of accuracy.

Management of reirradiations: A clinical and technical overview based on a French survey.

INTRODUCTION: The reirradiation number increased due to systemic therapies and patient survival. Few guidelines regarding acceptable cumulative doses to organs at risk (OARs) and appropriate dose accumulation tools need, made reirradiation challenging. The survey objective was to present the French current technical and clinical practices in reirradiations. METHODS: A group of physician and physicists developed a survey gathering major issues of the topic. The questionnaire consisted in 4 parts: data collection, demographic, clinical and technical aspects. It was delivered through the SFRO and the SFPM. Data collection lasted 2 months and were gathered to compute statistical analysis. RESULTS: 48 institutions answered the survey. Difficulties about patient data collection were related to patient safety, administrative and technical limitations. Half of the institutions discussed reirradiation cases during a multidisciplinary meeting. It mainly aimed at discussing the indication and the new treatment total dose (92%). 79% of the respondents used various references but only 6% of them were specific to reirradiations. Patients with pain and clinical deficit were ranked as best inclusion criteria. 54.2% of the institutions considered OARs recovery, especially for spinal cord and brainstem. A commercial software was used for dose accumulation for 52% of respondents. Almost all institutions performed equivalent dose conversion (94%). A quarter of the institutions estimated not to have the appropriate equipment for reirradiation. CONCLUSION: This survey showed the various approaches and tools used in reirradiation management. It highlighted issues in collecting data, and the guidelines necessity for safe practices, to increase clinicians confidence in retreating patients.

Dosimetric impact of 3D motion-compensated SPECT reconstruction for SIRT planning.

BACKGROUND: In selective internal radiation therapy, 99mTc SPECT images are used to optimize patient treatment planning, but they are affected by respiratory motion. In this study, we evaluated on patient data the dosimetric impact of motion-compensated SPECT reconstruction on several volumes of interest (VOI), on the tumor-to-normal liver (TN) ratio and on the activity to be injected. METHODS: Twenty-nine patients with liver cancer or hepatic metastases treated by radioembolization were included in this study. The biodistribution of 90Y is assumed to be the same as that of 99mTc when predictive dosimetry is implemented. A total of 31 99mTc SPECT images were acquired and reconstructed with two methods: conventional OSEM (3D) and motion-compensated OSEM (3Dcomp). Seven VOI (liver, lungs, tumors, perfused liver, hepatic reserve, healthy perfused liver and healthy liver) were delineated on the CT or obtained by thresholding SPECT images followed by Boolean operations. Absorbed doses were calculated for each reconstruction using Monte Carlo simulations. Percentages of dose difference (PDD) between 3Dcomp and 3D reconstructions were estimated as well as the relative differences for TN ratio and activities to be injected. The amplitude of movement was determined with local rigid registration of the liver between the 3Dcomp reconstructions of the extreme phases of breathing. RESULTS: The mean amplitude of the liver was 9.5 ± 2.7 mm. Medians of PDD were closed to zero for all VOI except for lungs (6.4%) which means that the motion compensation overestimates the absorbed dose to the lungs compared to the 3D reconstruction. The smallest lesions had higher PDD than the largest ones. Between 3D and 3Dcomp reconstructions, means of differences in lung dose and TN ratio were not statistically significant, but in some cases these differences exceed 1 Gy (4/31) and 8% (2/31). The absolute differences in activity were on average 3.1% ± 5.1% and can reach 22.8%. CONCLUSION: The correction of respiratory motion mainly impacts the lung and tumor doses but only for some patients. The largest dose differences are observed for the smallest lesions.

Performance study of a 360° CZT camera for monitoring 177Lu-PSMA treatment.

BACKGROUND: The aim of this study was to investigate the quantification performance of a 360° CZT camera for 177Lu-based treatment monitoring. METHODS: Three phantoms with known 177Lu activity concentrations were acquired: (1) a uniform cylindrical phantom for calibration, (2) a NEMA IEC body phantom for analysis of different-sized spheres to optimise quantification parameters and (3) a phantom containing two large vials simulating organs at risk for tests. Four sets of reconstruction parameters were tested: (1) Scatter, (2) Scatter and Point Spread Function Recovery (PSFR), (3) PSFR only and (4) Penalised likelihood option and Scatter, varying the number of updates (iterations × subsets) with CT-based attenuation correction only. For each, activity concentration (ARC) and contrast recovery coefficients (CRC) were estimated as well as root mean square. Visualisation and quantification parameters were applied to reconstructed patient image data. RESULTS: Optimised quantification parameters were determined to be: CT-based attenuation correction, scatter correction, 12 iterations, 8 subsets and no filter. ARC, CRC and RMS results were dependant on the methodology used for calculations. Two different reconstruction parameters were recommended for visualisation and for quantification. 3D whole-body SPECT images were acquired and reconstructed for 177Lu-PSMA patients in 2-3 times faster than the time taken for a conventional gamma camera. CONCLUSION: Quantification of whole-body 3D images of patients treated with 177Lu-PSMA is feasible and an optimised set of parameters has been determined. This camera greatly reduces procedure time for whole-body SPECT.

Evaluation of different algorithms for automatic segmentation of head-and-neck lymph nodes on CT images.

PURPOSE: To investigate the performance of 4 atlas-based (multi-ABAS) and 2 deep learning (DL) solutions for head-and-neck (HN) elective nodes (CTVn) automatic segmentation (AS) on CT images. MATERIAL AND METHODS: Bilateral CTVn levels of 69 HN cancer patients were delineated on contrast-enhanced planning CT. Ten and 49 patients were used for atlas library and for training a mono-centric DL model, respectively. The remaining 20 patients were used for testing. Additionally, three commercial multi-ABAS methods and one commercial multi-centric DL solution were investigated. Quantitative evaluation was assessed using volumetric Dice Similarity Coefficient (DSC) and 95-percentile Hausdorff distance (HD95%). Blind evaluation was performed for 3 solutions by 4 physicians. One recorded the time needed for manual corrections. A dosimetric study was finally conducted using automated planning. RESULTS: Overall DL solutions had better DSC and HD95% results than multi-ABAS methods. No statistically significant difference was found between the 2 DL solutions. However, the contours provided by multi-centric DL solution were preferred by all physicians and were also faster to correct (1.1 min vs 4.17 min, on average). Manual corrections for multi-ABAS contours took on average 6.52 min Overall, decreased contour accuracy was observed from CTVn2 to CTVn3 and to CTVn4. Using the AS contours in treatment planning resulted in underdosage of the elective target volume. CONCLUSION: Among all methods, the multi-centric DL method showed the highest delineation accuracy and was better rated by experts. Manual corrections remain necessary to avoid elective target underdosage. Finally, AS contours help reducing the workload of manual delineation task.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177 Lu-DOTATATE therapy.

BACKGROUND: Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship. PURPOSE: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data. METHODS: Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects. RESULTS: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 â†’ C3 in spleen and left kidney, and Ga,C.1 â†’ C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case. CONCLUSIONS: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues.

From netrin-1-targeted SPECT/CT to internal radiotherapy for management of advanced solid tumors.

Targeted radionuclide therapy is a revolutionary tool for the treatment of highly spread metastatic cancers. Most current approaches rely on the use of vectors to deliver radionuclides to tumor cells, targeting membrane-bound cancer-specific moieties. Here, we report the embryonic navigation cue netrin-1 as an unanticipated target for vectorized radiotherapy. While netrin-1, known to be re-expressed in tumoral cells to promote cancer progression, is usually characterized as a diffusible ligand, we demonstrate here that netrin-1 is actually poorly diffusible and bound to the extracellular matrix. A therapeutic anti-netrin-1 monoclonal antibody (NP137) has been preclinically developed and was tested in various clinical trials showing an excellent safety profile. In order to provide a companion test detecting netrin-1 in solid tumors and allowing the selection of therapy-eligible patients, we used the clinical-grade NP137 agent and developed an indium-111-NODAGA-NP137 single photon emission computed tomography (SPECT) contrast agent. NP137-111 In provided specific detection of netrin-1-positive tumors with an excellent signal-to-noise ratio using SPECT/CT imaging in different mouse models. The high specificity and strong affinity of NP137 paved the way for the generation of lutetium-177-DOTA-NP137, a novel vectorized radiotherapy, which specifically accumulated in netrin-1-positive tumors. We demonstrate here, using tumor cell-engrafted mouse models and a genetically engineered mouse model, that a single systemic injection of NP137-177 Lu provides important antitumor effects and prolonged mouse survival. Together, these data support the view that NP137-111 In and NP137-177 Lu may represent original and unexplored imaging and therapeutic tools against advanced solid cancers.

Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT.

PURPOSE: Deformable registration generally relies on the assumption that the sought spatial transformation is smooth. Yet, breathing motion involves sliding of the lung with respect to the chest wall, causing a discontinuity in the motion field, and the smoothness assumption can lead to poor matching accuracy. In response, alternative registration methods have been proposed, several of which rely on prior segmentations. We propose an original method for automatically extracting a particular segmentation, called a motion mask, from a CT image of the thorax. METHODS: The motion mask separates moving from less-moving regions, conveniently allowing simultaneous estimation of their motion, while providing an interface where sliding occurs. The sought segmentation is subanatomical and based on physiological considerations, rather than organ boundaries. We therefore first extract clear anatomical features from the image, with respect to which the mask is defined. Level sets are then used to obtain smooth surfaces interpolating these features. The resulting procedure comes down to a monitored level set segmentation of binary label images. The method was applied to sixteen inhale-exhale image pairs. To illustrate the suitability of the motion masks, they were used during deformable registration of the thorax. RESULTS: For all patients, the obtained motion masks complied with the physiological requirements and were consistent with respect to patient anatomy between inhale and exhale. Registration using the motion mask resulted in higher matching accuracy for all patients, and the improvement was statistically significant. Registration performance was comparable to that obtained using lung masks when considering the entire lung region, but the use of motion masks led to significantly better matching near the diaphragm and mediastinum, for the bony anatomy and for the trachea. The use of the masks was shown to facilitate the registration, allowing to reduce the complexity of the spatial transformation considerably, while maintaining matching accuracy. CONCLUSIONS: We proposed an automated segmentation method for obtaining motion masks, capable of facilitating deformable registration of the thorax. The use of motion masks during registration leads to matching accuracies comparable to the use of lung masks for the lung region but motion masks are more suitable when registering the entire thorax.

Modeling families of particle distributions with conditional GAN for Monte Carlo SPECT simulations.

Objective.We propose a method to model families of distributions of particles exiting a phantom with a conditional generative adversarial network (condGAN) during Monte Carlo simulation of single photon emission computed tomography imaging devices.Approach.The proposed condGAN is trained on a low statistics dataset containing the energy, the time, the position and the direction of exiting particles. In addition, it also contains a vector of conditions composed of four dimensions: the initial energy and the position of emitted particles within the phantom (a total of 12 dimensions). The information related to the gammas absorbed within the phantom is also added in the dataset. At the end of the training process, one component of the condGAN, the generator (G), is obtained.Main results.Particles with specific energies and positions of emission within the phantom can then be generated withGto replace the tracking of particle within the phantom, allowing reduced computation time compared to conventional Monte Carlo simulation.Significance.The condGAN generator is trained only once for a given phantom but can generate particles from various activity source distributions.

Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions. MATERIAL AND METHODS: All patients underwent iodine contrast-enhanced planning CT. Fourteen OAR were manually delineated. DL.1 and DL.2 solutions were trained with 63 mono-centric patients and > 1000 multi-centric patients, respectively. Ten and 15 patients with varied anatomies were selected for the atlas library and for testing, respectively. The evaluation was based on geometric indices (DICE coefficient and 95th percentile-Hausdorff Distance (HD95%)), time needed for manual corrections and clinical dosimetric endpoints obtained using automated treatment planning. RESULTS: Both DICE and HD95% results indicated that DL algorithms generally performed better compared with ABAS algorithms for automatic segmentation of HN OAR. However, the hybrid-ABAS (ABAS.3) algorithm sometimes provided the highest agreement to the reference contours compared with the 2 DL. Compared with DL.2 and ABAS.3, DL.1 contours were the fastest to correct. For the 3 solutions, the differences in dose distributions obtained using AS contours and AS + manually corrected contours were not statistically significant. High dose differences could be observed when OAR contours were at short distances to the targets. However, this was not always interrelated. CONCLUSION: DL methods generally showed higher delineation accuracy compared with ABAS methods for AS segmentation of HN OAR. Most ABAS contours had high conformity to the reference but were more time consuming than DL algorithms, especially when considering the computing time and the time spent on manual corrections.

Imaging of polychromatic sources through Compton spectral reconstruction.

Objective.Study the performance of a spectral reconstruction method for Compton imaging of polychromatic sources and compare it to standard Compton reconstruction based on the selection of photopeak events.Approach.The proposed spectral and the standard photopeak reconstruction methods are used to reconstruct images from simulated sources emitting simultaneously photons of 140, 245, 364 and 511 keV. Data are simulated with perfect and realistic energy resolutions and including Doppler broadening. We compare photopeak and spectral reconstructed images both qualitatively and quantitatively by means of activity recovery coefficient and spatial resolution.Main results.The presented method allows improving the images of polychromatic sources with respect to standard reconstruction methods. The main reasons for this improvement are the increase of available statistics and the reduction of contamination from higher initial photon energies. The reconstructed images present lower noise, higher activity recovery coefficient and better spatial resolution. The improvements become more sensible as the energy resolution of the detectors decreases.Significance.Compton cameras have been studied for their capability of imaging polychromatic sources, thus allowing simultaneous imaging of multiple radiotracers. In such scenarios, Compton images are conventionally reconstructed for each emission energy independently, selecting only those measured events depositing a total energy within a fixed window around the known emission lines. We propose to employ a spectral image reconstruction method for polychromatic sources, which allows increasing the available statistics by using the information from events with partial energy deposition. The detector energy resolution influences the energy window used to select photopeak events and therefore the level of contamination by higher energies. The spectral method is expected to have a more important impact as the detector resolution worsens. In this paper we focus on energy ranges from nuclear medical imaging and we consider realistic energy resolutions.