Notizie di Politica italiana - Pag. 399

Zampa: "Pronti a vaccinare i volontari a metà gennaio"Sondaggi, il gradimento del centrodestra è in crescitaSalvini con mascherina di Borsellino, critico il fratello del giudice

Crisi di Governo, c'è la data di quando Conte potrebbe cadereTumori,Guglielmo l'intelligenza artificiale individua nuove 'spie' per prevedere il ritorno cancro prostata E' questo il risultato di un nuovo studio condotto da ricercatori dello Human Technopole di Milano, dell’Institute of Cancer Research di Londra e del Royal Marsden Nhs Foundation Trust di Adnkronos   12-07-2024 - 15:43 Link copiato #iubenda-cs-banner .iub_ts_bc:not(.iubenda-custom-content) *,#iubenda-cs-banner [class*=" iub"],#iubenda-cs-banner [class^=iub],.iubenda-tp-btn:not([data-tp-nostyle]){ 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background-color: #fff; border-radius: 0.5rem; box-shadow: 0 12px 24px rgba(0, 0, 0, 0.12); font-family: sans-serif; content: 'Loading...'; display: flex !important; font-size: 13px; font-weight: bold; line-height: 1; justify-content: center; min-height: 50px; text-transform: uppercase;}#speakup-player:empty{ display: block;} Grazie all’intelligenza artificiale sono stati individuati nuovi marker, veri e propri 'campanelli d’allarme', per prevedere il rischio di recidiva del tumore alla prostata, anche a dieci anni di distanza: è questo il risultato di un nuovo studio condotto da ricercatori dello Human Technopole di Milano, dell’Institute of Cancer Research di Londra e del Royal Marsden Nhs Foundation Trust. I ricercatori hanno individuato, grazie all’uso di big data e Ai, che la compresenza di cellule tumorali con caratteristiche genetiche diverse all’interno dello stesso tumore e di differenze nella loro forma, dimensione e struttura è indicativa della capacità della neoplasia di cambiare nel tempo. A questa sua capacità evolutiva si associa, anche dopo un lasso di tempo molto lungo, un elevato rischio di ritorno della malattia. Lo studio è pubblicato oggi sulla nota rivista scientifica Nature Cancer. Questa ricerca, sottolineano gli scienziati, "potrà aiutare i medici a personalizzare meglio il trattamento per il cancro alla prostata, adottando trattamenti più aggressivi in quei casi in cui emerga, grazie a questi parametri, un maggiore rischio di recidiva. Per Andrea Sottoriva, responsabile del centro di ricerca in Biologia computazionale di Human Technopole e corresponding author dello studio. "oltre a produrre biomarcatori prognostici migliori per il cancro alla prostata, il nostro studio costituisce un’ulteriore prova delle possibilità predittive che derivano dallo studiare come uno stesso tumore si evolve e cambia nel tempo. Applicando un approccio computazionale a più set di dati, siamo stati in grado di decifrare alcune dinamiche della progressione del cancro e della resistenza al trattamento. Questo tipo di ricerca è fondamentale per approfondire la nostra comprensione di come e quando trattare i tumori, compreso il cancro alla prostata".Si tratta, "ancora di una sperimentazione e non di una pratica clinica - sottolinea Marino Zerial, direttore dello Human Technopolema - in futuro questo approccio potrebbe aiutare i medici a classificare sistematicamente i pazienti in base al rischio di recidiva della malattia e decidere quali terapie adottare. Ancora oggi rimane difficile prevedere gli esiti del cancro alla prostata perché la malattia presenta un’ampia eterogeneità, cioè esistono differenze significative tra le sue cellule, non solo nei diversi pazienti ma anche all’interno di ciascun tumore. Inoltre, la patologia spesso si sviluppa in più di un punto all’interno della ghiandola, producendo due o più tumori vicini. Di conseguenza, è spesso difficile per i medici determinare i migliori trattamenti per i pazienti".Studio diverso da precedenti per elevato numero di campioni analizzati e per analisi diverse fasi sviluppo tumorale. Questo studio si differenzia da altri lavori che in precedenza hanno misurato specifiche caratteristiche del tumore per prevederne gli esiti per l’elevato numero di campioni analizzati e per aver preso in esame la malattia in diverse fasi del suo sviluppo. Utilizzando il machine learning, i ricercatori hanno analizzato 1.923 campioni di 250 pazienti, concentrandosi sulla struttura spaziale del tessuto. Hanno anche utilizzato una tecnica di intelligenza artificiale appositamente creata per eseguire la classificazione di Gleason, un sistema di punteggio che classifica il tessuto canceroso da uno a cinque in base al modello delle sue cellule. Alle cellule cancerose che appaiono molto simili alle cellule sane viene assegnato il grado uno, mentre a quelle che sembrano significativamente diverse viene assegnato il grado cinque. Allo stesso tempo, i ricercatori hanno valutato le differenze genetiche tra le cellule all’interno dei singoli tumori, utilizzando 642 campioni provenienti da 114 partecipanti a studi di radioterapia al Royal Marsden. Questi campioni sono stati sovrapposti al gruppo citato sopra, fornendo ai ricercatori informazioni integrate sulla genomica e sulla morfologia delle cellule, e sulle condizioni dei pazienti nell’arco di oltre un decennio. I ricercatori hanno scoperto che le differenze genetiche e la diversità morfologica misurata dall’intelligenza artificiale (la differenza nella forma, dimensione e struttura delle cellule) sono indicative della capacità del tumore di evolversi, caratteristica che consente alla malattia di adattarsi e sopravvivere. Hanno misurato questa diversità osservando l’entità delle differenze tra le cellule in diverse aree del tumore, nota come eterogeneità intratumorale.I risultati hanno mostrato che questa 'evolubilità' era un forte predittore di recidiva, con la combinazione delle due misurazioni che identificava un sottogruppo di pazienti che presentavano recidiva della malattia nella metà del tempo rispetto al resto dei pazienti. I ricercatori hanno anche verificato una correlazione tra la perdita di un cromosoma specifico e una ridotta presenza di cellule immunitarie nel tumore, che potrebbe influenzare la sua risposta a determinati trattamenti. di Adnkronos   12-07-2024 - 15:43 Link copiato Commenti Leggi la Netiquette

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