This Sounds Like That

Le reti neurali sono ormai utilizzate in qualsiasi ambito e in differenti modi, con l’obiettivo di semplificare compiti di classificazione e di completare steps che possano facilitare i professionisti di vari settori. Quello che però molto spesso non va incontro all’utente è la mancata spiegazione della classificazione, dovuta in primis alla non linearità dei modelli adottati. Per questo motivo la maggior parte delle reti neurali viene trattata come “black box”. Se nel passato l’obiettivo era più mirato ad ottenere prestazioni elevate basandosi sulle metriche restituite dai vari classificatori, oggi si cerca di andare oltre e costruire sistemi quanto più user-friendly possibile, quindi interpretabili. Relativamente alla spiegazione, quando ci troviamo di fronte a compiti di classificazione di immagini, spesso illustriamo il nostro ragionamento sezionando l’immagine e sottolineando gli aspetti prototipici di una classe o dell’altra. Le prove raccolte per ciascuna di queste ci aiutano a prendere la nostra decisione finale. In questo lavoro, viene studiata un’architettura di deep learning – ProtoPNet – che ragiona in modo simile: la rete seziona l’immagine trovando le parti prototipiche e combina le prove dei prototipi per effettuare la classificazione finale. Il modello ragiona quindi in modo qualitativa- mente simile al modo in cui gli esperti spiegano come risolvere compiti di classificazione del suono. Il lavoro di tesi è però incentrato sulla “Sound-Recognition”, ovvero la capacità delle reti di simulare il procedimento umano di elaborazione dei dati partendo da una registrazione di suoni e trasformandoli in informazioni utili. L’obiettivo è quello di adattare l’architettura ProtoPNet al caso di segnali audio. In questo caso, gli audio vengono trasformati in spettrogrammi con il fine di ricercare prototipi “sonori” all’interno dell’audio, i quali caratterizzano le classi. La classificazione sarà quindi incentrata sì sui risultati delle solite metriche utilizzate, ma soprattutto sull’interpretabilità dei risultati. Abbiamo dimostrato l’efficacia del metodo sviluppato su datasets differenti in lunghezza e complessità del segnale

Complications of percutaneous endoscopic gastrostomy: a surgical experience

AIM: Percutaneous endoscopic gastrostomy (PEG) is a practical and safe option to place an alimentary gastrostomy. We observed that a relevant rate of complications are related to management of PEG. PATIENTS AND METHODS: We registered the patients treated in our Unit from September 1994 to December 2005. We placed 293 PEG (243 pts). Preferably using a tube 16 Fr, in 7 cases 18 Fr, in 21 cases 20 Fr and only in 3 cases 9 Fr. The median age was 69.8 years; ratio female:male 3:1. In 67 cases the treatment was carried out in not hospitalized patients. RESULTS: The incidence of late and early complications is statistically higher in hospitalized patients than at home. CONCLUSION: We think that a correct management of PEG (nurses correct information) and the experience of endoscopist and a dietician can significantly reduce the rate of complications

Retrospective survival analysis of stage II-III rectal cancer: tumour regression grade, grading and lymphovascular invasion are the only predictors

Background Tumour regression grade is gaining interest as a prognostic factor of patients undergoing neoadjuvant chemoradiotherapy and surgery for locally advanced rectal cancer.Methods A series of 68 consecutive patients with locally advanced rectal cancer treated by neoadjuvant chemoradiotherapy and surgery between 2010 and 2016 was retrospectively studied. The impact on disease-free survival (DFS) and overall survival (OS) of several criteria was analysed. Univariate analysis was performed through Kaplan-Meier statistics. Multivariate analysis was performed through Cox regression model. Using criteria found to be related to long-term outcomes, a predictive model of patient's OS was calculated.Results Poor tumour regression grade - TRG3 (P = 0.010), poor grading - G3 (P = 0.001) and lymphovascular invasion (LVI; P = 0.030) were associated with short OS at univariate analysis. OS was associated with TRG3 and G3 at multivariate analysis (P = 0.016 and P = 0.027, respectively). DFS was associated with LVI (P = 0.001), G3 tumours (P = 0.046) and TRG3 (P = 0.045) at univariate analysis. At multivariate analysis, only LVI was associated with DFS (P = 0.041). A score, pondering the impact of three parameters (2 points for TRG3, 2 for G3 and 1 for LVI), was created and resulted to predict patient OS (P = 0.008), ranging from 94.5 months (score = 0-1) to 32 months (score = 3-5).Conclusion TRG3 and G3 were associated with poor OS, and LVI was the most significant predictor of DFS. An easy-to-use score may allow for a more accurate prediction of OS

Stage III and metastatic Lymph Node Ratio are the only independent prognostic factors in colorectal signet-ring cell carcinoma patients

Background/aim: Signet-ring cell carcinoma (SRCC) is an uncommon histological variant of colorectal cancer (CRC). Knowledge is scarce due to its rarity. Our aim was to better evaluate the clinicopathologic and prognostic features of this little-known malignancy. Patients and methods: Thirty-nine consecutive patients with non-metastatic colorectal SRCC undergoing curative resection at University Hospital of Parma between 2000 and 2018 were examined in this retrospective analysis. Results: Mean overall (OS) and disease-free survival (DFS) were 33.6 and 31.5 months, respectively. At univariate analysis, the lymph-related parameters (nodal status, Stage III, metastatic lymph node ratio and lymphovascular invasion) were significantly associated with shorter OS and poorer DFS. At multivariate analysis, Stage III and a metastatic lymph node ratio ≥25% were found to be the only independent prognostic factors significantly correlated with worse OS and DFS. Conclusion: Nodal and lymphatic status should be carefully pondered when planning the most appropriate management of patients with colorectal SRCC