Deep learning for an improved diagnostic pathway of prostate cancer in a small multi-parametric magnetic resonance data regime

Prostate Cancer (PCa) is the second most commonly diagnosed cancer among men, with an estimated incidence of 1.3 million new cases worldwide in 2018. The current diagnostic pathway of PCa relies on prostate-specific antigen (PSA) levels in serum. Nevertheless, PSA testing comes at the cost of under-detection of malignant lesions and a substantial over-diagnosis of indolent ones, leading to unnecessary invasive testing such biopsies and treatment in indolent PCa lesions. Magnetic Resonance Imaging (MRI) is a non-invasive technique that has emerged as a valuable tool for PCa detection, staging, early screening, treatment planning and intervention. However, analysis of MRI relies on expertise, can be time-consuming, requires specialized training and in its absence suffers from inter and intra-reader variability and sub-optimal interpretations. Deep Learning (DL) techniques have the ability to recognize complex patterns in imaging data and are able to automatize certain assessments or tasks while offering a lesser degree of subjectiveness, providing a tool that can help clinicians in their daily tasks. In spite of it, DL success has traditionally relied on the availability of large amounts of labelled data, which are rarely available in the medical field and are costly and hard to obtain due to privacy regulations of patients’ data and required specialized training, among others. This work investigates DL algorithms specially tailored to work in a limited data regime with the final objective of improving the current prostate cancer diagnostic pathway by improving the performance of DL algorithms for PCa MRI applications in a limited data regime scenario. In particular, this thesis starts by exploring Generative Adversarial Networks (GAN) to generate synthetic samples and their effect on tasks such as prostate capsule segmentation and PCa lesion significance classification (triage). Following, we explore the use of Auto-encoders (AEs) to exploit the data imbalance that is usually present in medical imaging datasets. Specifically, we propose a framework based on AEs to detect the presence of prostate lesions (tumours) by uniquely learning from control (healthy) data in an outlier detection-like fashion. This thesis also explores more recent DL paradigms that have shown promising results in natural images: generative and contrastive self-supervised learning (SSL). In both cases, we propose specific prostate MRI image manipulations for a PCa lesion classification downstream task and show the improvements offered by the techniques when compared with other initialization methods such as ImageNet pre-training. Finally, we explore data fusion techniques in order to leverage different data sources in the form of MRI sequences (orthogonal views) acquired by default during patient examinations and that are commonly ignored in DL systems. We show improvements in a PCa lesion significance classification when compared to a single input system (axial view)

CrossTransUnet: A new computationally inexpensive tumor segmentation model for brain MRI

Brain tumors are usually fatal diseases with low life expectancies due to the organs they affect, even if the tumors are benign. Diagnosis and treatment of these tumors are challenging tasks, even for experienced physicians and experts, due to the heterogeneity of tumor cells. In recent years, advances in deep learning (DL) methods have been integrated to aid in the diagnosis, detection, and segmentation of brain neoplasms. However, segmentation is a computationally expensive process, typically based on convolutional neural networks (CNNs) in the UNet framework. While UNet has shown promising results, new models and developments can be incorporated into the conventional architecture to improve performance. In this research, we propose three new, computationally inexpensive, segmentation networks inspired by Transformers. These networks are designed in a 4-stage deep encoder-decoder structure and implement our new cross-attention model, along with separable convolution layers, to avoid the loss of dimensionality of the activation maps and reduce the computational cost of the models while maintaining high segmentation performance. The new attention model is integrated in different configurations by modifying the transition layers, encoder, and decoder blocks. The proposed networks are evaluated against the classical UNet network, showing that our networks have differences of up to an order of magnitude in the number of training parameters. Additionally, one of the models outperforms UNet, achieving training in significantly less time and with a Dice Similarity Coefficient (DSC) of up to 94%, ensuring high effectiveness in brain tumor segmentation.publishedVersio

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Background Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. Purpose To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. Study Type Retrospective. Population Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. Field Strength/Sequence 3-T, axial T2-weighted spin sequence. Assessment Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. Statistical Tests T-test, Mann–Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. Results After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32–6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975–0.987).publishedVersio

3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization.publishedVersio

Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection

Traditional deep learning (DL) approaches based on supervised learning paradigms require large amounts of annotated data that are rarely available in the medical domain. Unsupervised Out-of-distribution (OOD) detection is an alternative that requires less annotated data. Further, OOD applications exploit the class skewness commonly present in medical data. Magnetic resonance imaging (MRI) has proven to be useful for prostate cancer (PCa) diagnosis and management, but current DL approaches rely on T2w axial MRI, which suffers from low out-of-plane resolution. We propose a multi-stream approach to accommodate different T2w directions to improve the performance of PCa lesion detection in an OOD approach. We evaluate our approach on a publicly available data-set, obtaining better detection results in terms of AUC when compared to a single direction approach (73.1 vs 82.3). Our results show the potential of OOD approaches for PCa lesion detection based on MRI.Comment: Accepted and presented in ISBI 2023. To be published in Proceeding

Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach

An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools consider exclusively the axial MRI direction in spite of the availability as per acquisition protocols of multi-view data. Further, when multi-view data is exploited, manual annotations and availability at test time for all the views is commonly assumed. In this work, we explore a contrastive approach at training time to leverage multi-view data without annotations and provide flexibility at deployment time in the event of missing views. We propose a triplet encoder and single decoder network based on U-Net, tU-Net (triplet U-Net). Our proposed architecture is able to exploit non-annotated sagittal and coronal views via contrastive learning to improve the segmentation from a volumetric perspective. For that purpose, we introduce the concept of inter-view similarity in the latent space. To guide the training, we combine a dice score loss calculated with respect to the axial view and its manual annotations together with a multi-view contrastive loss. tU-Net shows statistical improvement in dice score coefficient (DSC) with respect to only axial view (91.25+-0.52% compared to 86.40+-1.50%,P<.001). Sensitivity analysis reveals the volumetric positive impact of the contrastive loss when paired with tU-Net (2.85+-1.34% compared to 3.81+-1.88%,P<.001). Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2.76+-1.89% compared to 3.92+-3.31%,P=.002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.Comment: Under revie

Factores asociados con el deterioro funcional en adultos mayores mexicanos

Introducción. El deterioro funcional está relacionado con muchos resultados adversos. Objetivo. Explorar la relación de los factores sociodemográficos, médicos y psicológicos con la incidencia del deterioro funcional en los adultos mayores mexicanos. Materiales y métodos. Se analizaron los datos de las cohortes de 2012 y 2015 de la encuesta del Estudio Mexicano de Salud y Envejecimiento. Se excluyeron los participantes con discapacidad funcional en el período de referencia (2012). Se evaluó de forma individual el deterioro funcional en las actividades básicas de la vida diaria (AVD) y en las instrumentales (AIVD). Resultados. Se encontró que el dolor, las comorbilidades, el nivel educativo, el estatus socioeconómico y la depresión se asociaban independientemente con el deterioro de las AVD. El deterioro de las AIVD se asoció con la edad, la educación deficiente, las comorbilidades, la depresión y el deterioro cognitivo. Conclusiones. La edad, el sexo, el estado financiero, el nivel educativo, el dolor y el número de comorbilidades se asociaron con la incidencia del deterioro funcional. El dolor tuvo una mayor asociación con la incidencia del deterioro funcional en las AVD a los tres años, en comparación con el deterioro cognitivo. El estudio del deterioro funcional por dominios permitió recabar información más detallada para determinar los factores que pueden intervenirse con el objetivo de reducir la incidencia del deterioro funcional y la dependencia.publishedVersio

In silico optimization of left atrial appendage Occluder implantation using interactive and modeling tools

Altres ajuts: This work was supported by the Retos I+D Programme (DPI2015-71640-R).According to clinical studies, around one third of patients with atrial fibrillation (AF) will suffer a stroke during their lifetime. Between 70 and 90% of these strokes are caused by thrombus formed in the left atrial appendage. In patients with contraindications to oral anticoagulants, a left atrial appendage occluder (LAAO) is often implanted to prevent blood flow entering in the LAA. A limited range of LAAO devices is available, with different designs and sizes. Together with the heterogeneity of LAA morphology, these factors make LAAO success dependent on clinician's experience. A sub-optimal LAAO implantation can generate thrombi outside the device, eventually leading to stroke if not treated. The aim of this study was to develop clinician-friendly tools based on biophysical models to optimize LAAO device therapies. A web-based 3D interactive virtual implantation platform, so-called VIDAA, was created to select the most appropriate LAAO configurations (type of device, size, landing zone) for a given patient-specific LAA morphology. An initial LAAO configuration is proposed in VIDAA, automatically computed from LAA shape features (centreline, diameters). The most promising LAAO settings and LAA geometries were exported from VIDAA to build volumetric meshes and run Computational Fluid Dynamics (CFD) simulations to assess blood flow patterns after implantation. Risk of thrombus formation was estimated from the simulated hemodynamics with an index combining information from blood flow velocity and complexity. The combination of the VIDAA platform with in silico indices allowed to identify the LAAO configurations associated to a lower risk of thrombus formation; device positioning was key to the creation of regions with turbulent flows after implantation. Our results demonstrate the potential for optimizing LAAO therapy settings during pre-implant planning based on modeling tools and contribute to reduce the risk of thrombus formation after treatment

Current professional standing of young medical oncologists in Spain : a nationwide survey by the Spanish Society of Medical Oncology + MIR section

There is a lack of knowledge about the career paths and employment situation of young medical oncologists. The aim of our study was to evaluate the current professional standing of these professionals in Spain. The Spanish Society of Medical Oncology + MIR section conducted a national online survey in May 2021 of young medical oncology consultants (< 6 years of expertise) and final year medical oncology residents. A total of 162 responses were eligible for analysis and included participants from 16 autonomous communities; 64% were women, 80% were consultants, and 20% were residents. More than half of the participants performed routine healthcare activity and only 7% research activity. Almost three quarters (73%) were subspecialized in a main area of interest and almost half of these chose this area because it was the only option available after residency. Half of the respondents (51%) considered working abroad and 81% believed the professional standing in Spain was worse than in other countries. After finishing their residency, only 22 were offered a job at their training hospital. Just 16% of participants had a permanent employment contract and 87% were concerned (score of ≥ 5 on a scale of 1-10) about their job stability. In addition, one quarter of the participants in our study showed an interest in increasing their research activity. The choice of subspecialty in medical oncology may depend on job opportunities after residency rather than personal interest. The abundance of temporary contracts may have influenced the job stability concerns observed. Future mentoring strategies should engage in building a long-term career path for young medical oncologists. The online version contains supplementary material available at 10.1007/s12094-022-02989-3