RESUMEN
Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging-Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.
RESUMEN
Phenotype can express different morphologies in response to biotic or abiotic environmental influences. Mollusks are particularly sensitive to different environmental parameters, showing macroscale shell morphology variations in response to environmental parameters. Few studies concern shell variations at the different scale levels along environmental gradients. Here, we investigate shell features at the macro, micro and nanoscale, in populations of the commercially important clam Chamelea gallina along a latitudinal gradient (~400 km) of temperature and solar radiation in the Adriatic Sea (Italian cost). Six populations of clams with shells of the same length were analyzed. Shells from the warmest and the most irradiated population were thinner, with more oval shape, more porous and lighter, showing lower load fracture. However, no variation was observed in shell CaCO3 polymorphism (100% aragonite) or in compositional and textural shell parameters, indicating no effect of the environmental parameters on the basic processes of biomineralization. Because of the importance of this species as commercial resource in the Adriatic Sea, the experimentally quantified and significant variations of mass and fracture load in C. gallina shells along the latitudinal gradient may have economic implications for fisheries producing different economical yield for fishermen and consumers along the Adriatic coastline.