Quality assurance for automatically generated contours with additional deep learning

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior

The Bacterial Mucosal Immunotherapy MV130 Protects Against SARS-CoV-2 Infection and Improves COVID-19 Vaccines Immunogenicity

COVID-19-specific vaccines are efficient prophylactic weapons against SARS-CoV-2 virus. However, boosting innate responses may represent an innovative way to immediately fight future emerging viral infections or boost vaccines. MV130 is a mucosal immunotherapy, based on a mixture of whole heat-inactivated bacteria, that has shown clinical efficacy against recurrent viral respiratory infections. Herein, we show that the prophylactic intranasal administration of this immunotherapy confers heterologous protection against SARS-CoV-2 infection in susceptible K18-hACE2 mice. Furthermore, in C57BL/6 mice, prophylactic administration of MV130 improves the immunogenicity of two different COVID-19 vaccine formulations targeting the SARS-CoV-2 spike (S) protein, inoculated either intramuscularly or intranasally. Independently of the vaccine candidate and vaccination route used, intranasal prophylaxis with MV130 boosted S-specific responses, including CD8+-T cell activation and the production of S-specific mucosal IgA antibodies. Therefore, the bacterial mucosal immunotherapy MV130 protects against SARS-CoV-2 infection and improves COVID-19 vaccines immunogenicity.CF was supported by AECC Foundation (INVES192DELF) and is currently funded by the Miguel Servet program (ID: CP20/00106) (ISCIII). IH-M receives the support of a fellowship from la Caixa Foundation (ID 100010434, fellowship code: LCF/BQ/IN17/11620074) and from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 713673. AJ-C is a postgraduate fellow of the City Council of Madrid at the Residencia de Estudiantes (2020–2021). GD is supported by an European Molecular Biology Organization (EMBO) Long-term fellowship (ALTF 379-2019). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. Project number 892965. OL and JA-C acknowledge Comunidad de Madrid (Tec4Bio-CM, S2018/NMT-4443, FEDER). Work in OL laboratory was funded by CNIO with the support of the projects Y2018/BIO4747 and P2018/NMT4443 from Comunidad de Madrid and co-funded by the European Social Fund and the European Regional Development Fund. The CNIO is supported by the Instituto de Salud Carlos III (ISCIII). Work at CNB and CISA is funded by the Spanish Health Ministry, Instituto de Salud Carlos III (ISCIII), Fondo COVID-19 grant COV20/00151, and Fondo Supera COVID-19 (Crue Universidades-Banco Santander) (to JG-A). Work in the DS laboratory is funded by the CNIC; by the European Research Council (ERC-2016-Consolidator Grant 725091); by Agencia Estatal de Investigación (PID2019-108157RB); by Comunidad de Madrid (B2017/BMD-3733 Immunothercan-CM); by Fondo Solidario Juntos (Banco Santander); by a research agreement with Inmunotek S.L.; and by Fundació La Marató de TV3 (201723). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the MICINN, and the Pro CNIC Foundation.Peer reviewe

Is whole‐body magnetic resonance imaging a source of anxiety in oncological patients?

Abstract Objective Magnetic resonance often produces feelings of anxiety before, or during, the examination. The aim of this study was to assess anxiety and potential causes of anxiety in cancer patients undergoing whole‐body magnetic resonance imaging (WB‐MRI). Methods This monocentric study recruited 70 cancer patients who were scheduled to undergo WB‐MRI for detection, staging or therapy monitoring. At baseline (prior to the WB‐MRI), assessments were performed using the State–Trait Anxiety Inventory (STAI‐Y 1), Illness Perception Questionnaire (IPQ‐R), Big Five Inventory (BIF‐10) and Revised Life Orientation Test (LOT‐R), while at the end of the WB‐MRI examination the patients repeated the STAI‐Y 1 questionnaire and were asked to indicate their preference between WB‐MRI and computed tomography. Results We found a positive correlation between pre‐ and post‐examination STAI‐Y 1 scores (r = 0.536, p < .0001), with no significant difference between them. Pre‐examination STAI‐Y 1 scores had a negative correlation with the emotional stability in the BIF‐10 questionnaire (r = −0.47, p = .001) and a positive correlation with emotional representation (r = 0.57, p = .001) in IPQ‐R. The post‐examination STAI‐Y 1 had a negative correlation with optimistic orientation (r = −0.59, p = .001). Conclusions The anxiety associated with a WB‐MRI examination was only in small part associated with the examination itself, and in fact, most patients preferred WB‐MRI to computed tomography. Concern with the outcome of the examination was likely a greater source of anxiety

Investigating cancer patient acceptance of Whole Body MRI

Background: Whole Body magnetic resonance imaging (WB-MRI) enables early cancer detection, without exposing the patient to ionizing radiation. Our aim was to investigate patients’ acceptance of WB-MRI as a procedure for cancer staging and follow up. Materials and methods: 135 oncologic subjects participated to the study. An ad hoc questionnaire was administered before and after WB-MRI, to assess patient's confidence and concerns about WB-MRI, psychological reactions, experience and perceived utility of the procedure. Results: Before undergoing WB-MRI, about 58% of the patients were concerned for cancer progression outcome. 80.4% felt that they were given good information about the exam and the most informed group also perceived and higher level of utility of WB-MRI and no risk. Among people reporting discomfort with the exam (51.9%) the main reasons were noise and exam duration. Despite this, 80% of patients expressed high levels of satisfaction, and the majority (69%) judged WB-MRI more acceptable than other diagnostic exams. Patients who believed to have received more information before the exam rated their global satisfaction higher. Conclusion: Our results show that WB-MRI examinations were well-accepted and perceived with high levels of satisfaction by most patients. WB-MRI appears to be equally or more tolerable than other total body imaging modalities (e.g. PET, CT), especially if they receive enough information from the radiologist

Controlling von Milchrinderzuchtprogrammen

Das Ziel dieser Arbeit war es, Controllingansätze für Milchrinderzuchtprogramme zu entwickeln. Aus diesem Grund wurden verschiedene Themenkomplexe aus der praktischen Milchrinderzucht untersucht. Zunächst wurde der Einfluss von Genetik und Umwelt auf Auktionspreise von erstlaktierenden Holsteinkühen analysiert. Im Anschluss wurde eine Strategie zur Selektion von informativen Testherden entwickelt und deren Einfluss auf ein Zuchtprogramm für Holsteinkühe wurde betrachtet. Abschließend wurde ein Ansatz zum Management der genetischen Variabilität von Milchviehpopulationen auf eine Auswahl von potentiellen Bullenmüttern und -vätern angewandt. Hierbei wurden sowohl die additiv-genetischen als auch die genomische Verwandtschaftskoeffiizienten der Tiere genutzt

CT-based radiomics and deep learning for BRCA mutation and progression-free survival prediction in ovarian cancer using a multicentric dataset

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results

MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer

High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by &ldquo;ProMisE&rdquo;. This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management