56 research outputs found
Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer
No full text: In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients' treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure
MP53-14 MULTIMODAL TREATMENT FOR HIGH-RISK PROSTATE CANCER WITH HIGH-DOSE INTENSITY-MODULATED RADIATION THERAPY, CONCURRENT INTENSIFIED-DOSE DOCETAXEL AND LONG-TERM ANDROGEN DEPRIVATION THERAPY AFTER RADICAL PROSTATECTOMY AND LYMPHADENECTOMY: RESULTS OF A PROSPECTIVE PHASE II TRIAL
No full textComparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
No full textIn recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented
NEW DEVELOPMENTS IN LIDAR UAS SURVEYS. PERFORMANCE ANALYSES AND VALIDATION OF THE DJI ZENMUSE L1
No full textThanks to the latest technological developments LiDAR (Light Detection And Ranging) sensors are no longer an exclusive feature of manned airborne platforms but they are close to becoming a commercial solution in the UAS (Uncrewed Aerial Systems) domain. The release on the market of the Zenmuse L1 by DJI (Dà-Jiāng Innovations) is a step further in this direction, thanks also to a substantial work of enhancement made by the Chinese company not only on the hardware side, but also on the software one. The research presented in this work is focused on the use of the L1 LiDAR for the 3D survey of built heritage, analysing the results of different tests to highlight first considerations on its performances and the point cloud quality. Considering its recent release, this sensor is still yet to be thoroughly analysed and validated and its performances to be assessed. LiDAR data has been acquired on a selected test site, documented also with traditional Terrestrial Laser Scanner (TLS) and UAS photogrammetry. The latter techniques (supported also by a topographic survey) will thus be exploited to generate the ground reference and to assess the quality and accuracy of the L1 dataset
Comparing machine and deep learning methods for large 3D heritage semantic segmentation
No full textIn recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented
Use of 18F-choline positron emission tomography/CT in high-risk prostate cancer: A case of solitary adrenal metastasis
No full textComputed tomography and magnetic resonance imaging detected an isolated adrenal lesion in an elderly man with high-risk prostate cancer who was undergoing radiotherapy (RT) and hormonal therapy (HT). When prostatespecific antigen (PSA) was 31.66 ng/mL, the lesion was not identified as a metastasis by 18F-choline positron emission tomography/computed tomography (18F-choline-PET/CT). When PSA was over 100 ng/mL, 18F-choline-PET/ CT diagnosed the malignancy. After adrenalectomy, PSA returned to normal, and stable disease remission was obtained. This case suggests that atypical metastasis may be underdiagnosed
High-level-of-detail semantic 3D GIS for risk and damage representation of architectural heritage
No full textThe need to share information about architectural heritage effectively after a disaster event, in order to foster its preservation, requires the use of a common language between the involved actors and stakeholders. A database able to connect the architectural heritage representation with the data useful for hazard and risk analysis can thus be a powerful instrument. This paper outlines a methodology to represent 3D models of the architectural heritage, according to some existing standards data models, and relate their geometric features to the damage mechanisms that could occur after an earthquake. Among all the existing standard to represent cartographic, cultural heritage and hazard/risk information, respectively INSPIRE, CityGML, UNESCO, CIDOC-CRM, its extension MONDIS and the Getty Institute vocabularies, compliant to the CIDOC-CRM, have been taken into account. An INSPIRE extension has been proposed for increasing the level of detail (LoD) of the representation and improving the description of heritage buildings, adding some macro-elements and elements “feature types” connected with the damage mechanisms, identified in structural studies. The suggested method allows to archive, in a multi-scale database, 3D information with a very high level of detail about architectural heritage and can help structural engineers and conservator-restorers in preventing further damages through individuating useful targeted actions.Urban Data Scienc
Un benchmark per la segmentazione semantica di nuvole di punti di beni culturali
No full textLa mancanza di dati di benchmark per la segmentazione semantica di nuvole di punti dei beni culturali sta ostacolando lo sviluppo di soluzioni di classificazione automatica in questo campo. I dati 3D e le nuvole di punti del nostro patrimonio culturale rappresentano strutture geometriche complesse con classi non convenzionali, le quali impediscono la semplice implementazione dei metodi già disponibili, sviluppati in altri campi o per altri tipi di dati. La segmentazione semantica dei dati 3D del patrimonio aiuterebbe la comunità nella migliore comprensione e analisi dei gemelli digitali (digital twins), faciliterebbe le operazioni di salvaguardia e supporterebbe molte altre attività legate al settore dei beni culturali. In questo contributo si presenta il primo benchmark con milioni di punti 3D annotati manualmente appartenenti a scenari del patrimonio, realizzati per facilitare lo sviluppo, l'addestramento, il test e la valutazione di metodi e algoritmi di apprendimento automatico nel campo dei beni architettonici. Il benchmark proposto, disponibile su http://archdataset.polito.it/, comprende set di dati e risultati di classificazione finalizzati a migliorare i confronti e approfondire i punti di forza e di debolezza dei diversi approcci di machine e deep learning per la segmentazione semantica della nuvola di punti del patrimonio, oltre a promuovere una forma di crowdsourcing per arricchire il database già annotato
Microencapsulated Sodium Butyrate in the Prevention of Acute Radiotherapy Proctitis: Single-Center Prospective Study
No full textBackground/Objectives: Prostate cancer is the most frequent cancer in men, for which Radiotherapy (RT) is used as a radical or post-surgical treatment. Actinic proctitis is one of the most disabling side effects of RT. Intestinal microbiome studies have highlighted the importance of short-chain fatty acids, in particular butyric acid, for their beneficial effects over intestinal epithelial cells. The aim of this prospective study is to evaluate if treatment with micro-encapsulated sodium butyrate (MESB) can reduce the incidence of actinic proctitis during RT in prostate cancer patients. Methods: In total, 122 consecutive patients with prostate cancer treated in Radiotherapy Unit, Centro di Riferimento Oncologico, IRCCS Aviano, were enrolled. Patients received MESB (3 tablets/day) from one week before until four weeks after RT. They completed a diary, tracking daily bowel movements, rectal bleeding, abdominal pain, and perceived health status before, at the end, and one month after RT. Results: Although an improvement in symptoms was observed, when comparing interpatient data before RT vs. one month after the end of RT, statistically significant differences emerged only regarding abdominal pain (94.2% vs. 81.6% vs. 81.6%) (McNemar’s test p < 0.002). Conclusions: MESB appears effective in reducing radiation-induced bowel toxicity during RT, minimizing stool changes, incontinence, and abdominal pain. Although patients’ health perception declined at RT completion, it improved after one month, suggesting MESB may support clinical recovery post-treatment
A 3D packaging technology for acoustically optimized integration of 2D CMUT arrays and front end circuits
No full textAs compared to piezoelectric technology, MEMS technology employed for Capacitive Micromachined Ultrasonic Transducer (CMUT) fabrication provides increased compatibility with 3D packaging methods, enabling the possible development of advanced transducer-electronics multi-chip modules (MCM) for medical imaging applications. In this paper, an acoustically optimized 3D packaging method for the interconnection of Reverse-Fabricated 2D CMUT arrays and front end ICs using a wafer-level compatible process is presented. The developed packaging method uses Cu pillars and Sn-Ag solder reflow for electrical interconnection, and patterned Benzocy-clobutene (BCB) for mechanical bonding. Process parameters were optimized by analyzing the acoustic behavior of a CMUT supported by a BCB film laying on a silicon substrate using Finite Element Modeling (FEM). Dummy CMUT and ASIC wafers were processed and MCMs were assembled following a chip-to-chip bonding approach using the optimized process parameters. Electrical characterization of the MCMs demonstrated successful contact across the entire fabricated devices. Probe head prototypes were assembled and pulse-echo experiments were carried out using the MCM surface as a reflector to verify the effectiveness of the optimization on the acoustic behavior of the device
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