A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.

Purpose/objectives: An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care. Materials and method: In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics. Results: The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs. Conclusion: This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy.

BACKGROUND AND PURPOSE: MR-guided radiotherapy (MRgRT) online plan adaptation accounts for tumor volume changes, interfraction motion and thus allows daily sparing of relevant organs at risk. Due to the high interfraction variability of bladder and rectum, patients with tumors in the pelvic region may strongly benefit from adaptive MRgRT. Currently, fast automatic annotation of anatomical structures is not available within the online MRgRT workflow. Therefore, the aim of this study was to train and validate a fast, accurate deep learning model for automatic MRI segmentation at the MR-Linac for future implementation in a clinical MRgRT workflow. MATERIALS AND METHODS: For a total of 47 patients, T2w MRI data were acquired on a 1.5 T MR-Linac (Unity, Elekta) on five different days. Prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, body and bony structures were manually annotated. These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of ± 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable. RESULTS: The quantitative evaluation revealed a maximum median 95% HD of 6.9 mm for the rectum and minimum median 95% HD of 2.7 mm for the bladder. Maximal and minimal median DSC were detected for bladder with 0.97 and for penile bulb with 0.73, respectively. Using a tolerance level of 3 mm, the highest and lowest sDSC were determined for rectum (0.94) and anal canal (0.68), respectively. Qualitative evaluation resulted in a mean score of 1.2 for AICs over all organs and patients across all expert ratings. For the different autocontoured structures, the highest mean score of 1.0 was observed for anal canal, sacrum, femur left and right, and pelvis left, whereas for prostate the lowest mean score of 2.0 was detected. In total, 80% of the contours were rated be clinically acceptable, 16% to require minor and 4% major adjustments for online adaptive MRgRT. CONCLUSION: In this study, an AI-based autocontouring was successfully trained for online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac system. The developed model can automatically generate contours accepted by physicians (80%) or only with the need of minor corrections (16%) for the irradiation of primary prostate on the clinically employed sequences.

A DCE-MRI Driven 3-D Reaction-Diffusion Model of Solid Tumor Growth.

Predicting tumor growth and its response to therapy remains a major challenge in cancer research and strongly relies on tumor growth models. In this paper, we introduce, calibrate, and verify a novel image-driven reaction-diffusion model of avascular tumor growth. The model allows for proliferation, death and spread of tumor cells, and accounts for nutrient distribution and hypoxia. It is constrained by longitudinal time series of dynamic contrast-enhancement-MRI images. Tumor specific parameters are estimated from two early time points and used to predict the spatio-temporal evolution of the tumor volume and cell densities at later time points. We first test our parameter estimation approach on synthetic data from 15 generated tumors. Our in silico study resulted in small volume errors (<5%) and high Dice overlaps (>97%), showing that model parameters can be successfully recovered and used to accurately predict the tumor growth. Encouraged by these results, we apply our model to seven pre-clinical cases of breast carcinoma. We are able to show promising preliminary results, especially for the estimation for early time points. Processes like angiogenesis and apoptosis should be included to further improve predictions for later time points.

[Laboratorial alteration preceding staphylococcal infection clinical manifestations after intestinal transplantation]. / Alteración laboratorial que precede a las manifestaciones clínicas de infección estafilocócica postrasplante intestinal.

BACKGROUND: Hospital-acquired infection, often with Staphylococcus aureus, is an important complication in intestinal transplant. CLINICAL CASE: A 2-year-old girl underwent small bowel transplantation owing to a small bowel volvulus. On the first postoperative day, lymphocyte phenotypes, serum immunoglobulins and chemotactic and phagocytic activity of neutrophils were assessed in peripheral blood. A decrease in the ingestion phase of phagocytosis by neutrophils was identified, in comparison with the results of 20 healthy children. On the second day, the patient had low fever and, on the third, abdominal pain. In view of this, she underwent a laparotomy that revealed purulent ascites due to Staphylococcus aureus. Specific treatment resulted in rapid regression of the infectious condition and good evolution of the patient. CONCLUSIONS: A decrease in the ingestion stage of phagocytosis by neutrophils preceded staphylococcal purulent ascites clinical manifestations, and immunologic assessment contributed to early diagnosis and treatment of the infection. We believe evaluation of neutrophilic activity is important in patients undergoing intestinal transplantation in order for possible hospital-acquired infections to be early diagnosed.

Antecedentes: La infección hospitalaria, frecuentemente por Staphylococcus aureus, es una complicación importante en los pacientes con trasplante intestinal. Caso clínico: Niña de 2 años de edad sometida a trasplante de intestino delgado debido a vólvulo yeyunal. En el primer día del posoperatorio, en la sangre periférica fueron evaluados fenotipo de linfocitos, inmunoglobulinas séricas, actividad quimiotáctica y fagocitaria de neutrófilos. Se identificó disminución de la etapa de ingestión de fagocitosis neutrofílica, en comparación con los resultados de 20 niños saludables. En el segundo día, la paciente presentó fiebre baja y en el tercero, dolor abdominal. Debido a lo anterior fue sometida a laparotomía que reveló ascitis purulenta por Staphylococcus aureus. El tratamiento específico derivó en regresión rápida del cuadro infeccioso y buena evolución. Conclusiones: La disminución de la etapa de ingestión de la fagocitosis neutrofílica precedió a las manifestaciones clínicas de ascitis purulenta estafilocócica; la evaluación inmunológica contribuyó al diagnóstico y tratamiento precoces de la infección. Creemos que es importante la evaluación de la actividad neutrofílica en pacientes sometidos a trasplante intestinal, con la finalidad de diagnosticar tempranamente posibles infecciones hospitalarias.

A DCE-MRI imaging-based model for simulation of vascular tumour growth.

Imaging-based modelling of tumour growth can serve as a powerful tool to understand and predict tumour evolution and its response to therapy. The purpose of this study was to introduce, calibrate and evaluate a multi-scale model of vascular tumour growth. The model allows for proliferation, death and spatial spread of tumour cells as well as for new vessel creation. Both the calibration and the evaluation of the tumour growth model were performed using pre-clinical longitudinal time series of dynamic contrast-enhanced magnetic resonance imaging of colon carcinoma. Tumour specific model parameters, extracted from the images at two subsequent time points, were included into the model to predict the spatio-temporal evolution of the tumour at a third point in time. Simulation results for three pre-clinical cases demonstrated the model's ability to simulate the cellular as well as the 2D evolution of the tumour.