Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice.

El cáncer gástrico continúa siendo un problema de gran relevancia para los oncólogos médicos dada su agresividad y la dificultad para diagnosticarse en estadios precoces. A pesar del desarrollo de combinaciones de quimioterapia y de la aparición de segundas y sucesivas líneas, las ganancias en supervivencia global son escasas. El estudio de los inhibidores de punto de control inmunológico y la definición de subgrupos moleculares en cáncer gástrico subsidiarios de responder a tratamientos inmunomediados está en auge en los últimos años. Así, la inmunoterapia presenta resultados prometedores en términos de supervivencia, tasa de respuestas y tolerancia, en subgrupos seleccionados.

Fu C, Stoeckle JH, Masri L, Pandey A, Cao M, Littman D, Rybstein M, Saith SE, Yarta K, Rohatgi A, Makarov DV, Sherman SE, Morrissey C, Jordan AC, Razzo B, Theprungsirikul P, Tsai J, Becker DJ.Cancer. 2021 Sep 15;127(18):3466-3475. doi: 10.1002/cncr.33657. Epub 2021 Jun 7.PMID: 34096048

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis.

The Georgia Institute of Technology and Ovarian Cancer Institute researchers are using machine learning algorithms to predict how patients will respond to cancer-fighting drugs. Advances in machine learning and artificial intelligence are allowing researchers to create more targeted precision medicine-based treatment using predictive analytics. By analyzing large amounts of complex data, clinicians can provide individualized treatments, improving patient outcomes.

El cáncer gástrico (CG) es la quinta neoplasia más frecuente a escala mundial. Presenta una gran variedad geográfica y un comportamiento agresivo que le confiere un pronóstico pobre. A pesar de los avances de los últimos años en el conocimiento molecular de esta enfermedad, aún no somos capaces de traducir estos en una mejora de la supervivencia, salvo en el grupo de pacientes con enfermedad avanzada HER2+. A continuación revisaremos los estudios más importantes, centrándonos en la enfermedad avanzada, con terapias dirigidas contra la vía HER2, angiogénesis y otros inhibidores de las vías EGFR, MET, PARP, FGFR, claudinas, Pi3K o los resultados obtenidos con TAS-102.

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

Russo D, Polverelli N, Malagola M, Farina M, Leoni A, Bernardi S, Mammoliti S, Sacchi N, Martino M, Ciceri F; GITMO Centers. Bone Marrow Transplant. 2021 Sep;56(9):2272-2275. doi: 10.1038/s41409-021-01287-w. Epub 2021 Apr 19.PMID: 33875810

Background: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development.

Aim: This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail.

Methods: A literature search to identify important studies exploring the use of AI in colonoscopy was performed.
Results: This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.

Findings from samples of the AIO ColoPredictPlus 2.0 registry study