Mattia Sanna Montanelli

Colorectal cancer: Biology and pathology

Faa, Gavino;Pretta, Andrea
Secondo
Writing – Original Draft Preparation
;
Fraschini, Matteo;Cau, Flaviana;Balestrieri, Antonella;Scartozzi, Mario
Penultimo
Supervision
;
Saba, Luca
Ultimo
Supervision
2025-01-01

Abstract

Colorectal cancer (CRC) presents significant challenges in diagnosis and treatment due to its complex biology and pathology. Recent advancements in personalized medicine have transformed the role of pathologists from traditional morphologists to critical clinical consultants for multidisciplinary teams, including oncologists, surgeons, and geneticists. Pathologists now provide prognostic insights beyond traditional staging, such as identifying microsatellite instability (MSI) and gene mutations like KRAS and BRAF. The integration of immunohistochemistry and molecular testing has enhanced the diagnostic and prognostic accuracy, facilitating targeted therapies, especially for MSI-high tumors that benefit from immunotherapy. This chapter highlights the histopathological features of CRC and precursor lesions, such as adenomas with dysplasia, and explores familial syndromes like Lynch syndrome and mutY DNA glycosylase (MUTYH)-associated polyposis, emphasizing their genetic underpinnings and cancer risks. Moreover, the chapter discusses the application of artificial intelligence (AI) and deep learning (DL) in analyzing histological images, predicting MSI status, and identifying key mutations directly from tissue sections, heralding a new era in computational pathology. These technological advancements promise to streamline the diagnostic process, allowing for rapid and precise treatment strategies. Ultimately, this comprehensive approach aims to optimize patient outcomes through personalized care in CRC management.
2025
Inglese
Colorectal Imaging: From Basic to Advanced Concepts
Saba Luca
Saba Luca
3
15
13
Elsevier
Amsterdam
9780443290480
Esperti anonimi
internazionale
scientifica
Artificial intelligence (AI)
Colorectal cancer (CRC)
Deep learning (DL)
Lynch syndrome
Microsatellite instability (MSI)
Goal 3: Good health and well-being
info:eu-repo/semantics/bookPart
2.1 Contributo in volume (Capitolo o Saggio)
Faa, Gavino; Pretta, Andrea; Fraschini, Matteo; Cau, Flaviana; Coghe, Ferdinando; Balestrieri, Antonella; Van Eyken, Peter; Castagnola, Massimo; Scart ...espandi
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
10
268
none
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie