The Crying Need for a Better Response Assessment in Rectal Cancer

Opinion statement Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch a...

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Bibliographic Details
Published in:Current treatment options in oncology 2023-11, Vol.24 (11), p.1507-1523
Main Authors: Amintas, Samuel, Giraud, Nicolas, Fernandez, Benjamin, Dupin, Charles, Denost, Quentin, Garant, Aurelie, Frulio, Nora, Smith, Denis, Rullier, Anne, Rullier, Eric, Vuong, Te, Dabernat, Sandrine, Vendrely, Véronique
Format: Article
Language:English
Subjects:
Biomarkers
Chemoradiotherapy - methods
Colorectal cancer
Computed tomography
Crying
Humans
Medicine
Medicine & Public Health
Neoadjuvant Therapy - methods
Oncology
Patients
Positron Emission Tomography Computed Tomography
Prediction models
Preservation
Radiomics
Rectal Neoplasms - diagnostic imaging
Rectal Neoplasms - therapy
Rectum
Retrospective Studies
Topical Collection on Lower Gastrointestinal Cancers
Treatment Outcome
Tumors
Watchful Waiting - methods
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Summary:Opinion statement Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients’ clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers. Graphical Abstract
ISSN:1527-2729
1534-6277
1534-5277
DOI:10.1007/s11864-023-01125-9