New artificial intelligence (AI) capabilities may make it possible to improve the effectiveness of brachytherapy for men with prostate cancer (PCa) by almost instantly generating dosage plans, according to investigators. In a typical high-dose rate (HDR) brachytherapy procedure for PCa, needle applicators are first inserted by the physician to the tumor target. A planner then develops a treatment plan manually. During this time the patient carries the needles, waiting for the planning to finish. With the current standard of care, it takes up to an hour or more to generate a high-quality plan.
New Delhi: Advances in technology and medical research could mean seismic changes in the healthcare industry. Soon, cancer, heart disease, diabetes and other debilitating illnesses could be defeated - perhaps, 20 years from now (unbelievable as it may sound) - thanks to scientists, medical doctors and researchers who are working vigorously, making stupendous progress on all these fronts. Over the last decade, healthcare is one of the industries that has evolved the most, yet, we're going to see changes in the way diseases are being treated. It's evident that we're going to witness drastic changes in a number of dimensions - from robots in the role of healthcare professionals to smart technology and artificial intelligence tools that will improve the quality of care and population health. Dr Sanjay Pandey, Head - Andrology and Reconstructive Urology - Kokilaben Dhirubhai Ambani Hospital, Mumbai, spoke on how digital technology, robotics and AI are transforming the face of medicine.
"Our results show that it is possible to train an AI system to detect and grade prostate cancer on the same level as leading experts," study author Martin Eklund, with Karolinska Institutet in Sweden, said in a statement. "This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases."
LONDON: Scientists have developed an artificial intelligence (AI) based method that is as good at identifying and grading prostate cancer as world-leading uro-pathologists. The AI-system has the potential to solve one of the bottlenecks in today's prostate cancer histopathology by providing more accurate diagnosis and better treatment decisions, according to the study published in The Lancet Oncology journal. "Our results show that it is possible to train an AI-system to detect and grade prostate cancer on the same level as leading experts," said Martin Eklund, an associate professor at Karolinska Institutet in Sweden. "This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases," Eklund said. A problem in today's prostate pathology is that there is a certain degree of subjectivity in the assessments of the biopsies, researchers said.
We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.
The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to'big data' enables the'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
Rundo, Leonardo, Han, Changhee, Zhang, Jin, Hataya, Ryuichiro, Nagano, Yudai, Militello, Carmelo, Ferretti, Claudio, Nobile, Marco S., Tangherloni, Andrea, Gilardi, Maria Carla, Vitabile, Salvatore, Nakayama, Hideki, Mauri, Giancarlo
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of Convolutional Neural Networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.
With more and more VR content popping up every day, viewers need the latest hardware and software to experience it all. The app is currently in development, and would give audiences a centralized resource for all 360-degree and VR content. He also gives an assessment of the current state of the VR industry. Vergara points to promising earnings reports from the primary VR content and hardware manufacturers as indicators that VR is becoming mainstream. However, he warns that any advancements to the technology are moot without a growing and prestigious content library.
In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients (ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate cancer (PCa) via semi-supervised adversarial learning. Specifically, our synthesizer generates mp-MRI data in a sequential manner: first generating ADC maps from 128-d latent vectors, followed by translating them to the T2w images. The synthesizer is trained in a semisupervised manner. In the supervised training process, a limited amount of paired ADC-T2w images and the corresponding ADC encodings are provided and the synthesizer learns the paired relationship by explicitly minimizing the reconstruction losses between synthetic and real images. To avoid overfitting limited ADC encodings, an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images. To improve the robustness of synthesizing, we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only. A StitchLayer is then employed to fuse sub-images together in an interlaced manner into a full-size image. To enforce the synthetic images to indeed contain distinguishable CS PCa lesions, we propose to also maximize an auxiliary distance of Jensen-Shannon divergence (JSD) between CS and nonCS images. Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship. Compared to the state-of-the-art synthesis methods, our method achieves a significant improvement in terms of both visual and quantitative evaluation metrics.