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microRNAs combined to radiomic features as a predictor of complete clinical response after neoadjuvant radio-chemotherapy for locally advanced rectal cancer: a preliminary study

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To define a predictive Artificial Intelligence (AI) algorithm based on the integration of a set of biopsy-based microRNAs expression data and radiomic features to understand their potential impact in predicting clinical response (CR) to neoadjuvant radio-chemotherapy (nRCT).

Summary background data

The identification of patients who would truly benefit from nRCT for Locally Advanced Rectal Cancer (LARC) could be crucial for an improvement in a tailored therapy.


Forty patients with LARC were retrospectively analyzed. An MRI of the pelvis before and after nRCT was performed. In the diagnostic biopsy, the expression levels of 7 miRNAs were measured and correlated with the tumor response rate (TRG), assessed on the surgical sample. The accuracy of complete CR (cCR) prediction was compared for i) clinical predictors; ii) radiomic features; iii) miRNAs levels; and iv) combination of radiomics and miRNAs.


Clinical predictors showed the lowest accuracy. The best performing model was based on the integration of radiomic features with miR-145 expression level (AUC-ROC = 0.90). AI algorithm, based on radiomics features and the overexpression of miR-145, showed an association with the TRG class and demonstrated a significant impact on the outcome.


The pre-treatment identification of responders/NON-responders to nRCT could address patients to a personalized strategy, such as total neoadjuvant therapy (TNT) for responders and upfront surgery for non-responders. The combination of radiomic features and miRNAs expression data from images and biopsy obtained through standard of care has the potential to accelerate the discovery of a noninvasive multimodal approach to predict the cCR after nRCT for LARC.

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The manuscript has not been published nor is considered for publication or elsewhere in any language. There are no other works in preparation, submitted, in press, or published that are potentially overlapping the actual presented report.


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Dr. Matilde Costa for the support with Radiomics Health-Myne software; Dr. Laura Bernardi, MD and Dr. Paola Germani, MD for a preliminary draft of the paper and review of literature; and Dr. Eugenia Capozzella, MD  for English language review.


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Correspondence to Pasquale Losurdo.

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Drs. Pasquale Losurdo, Ilaria Gandin, Manuel Belgrano, Ilaria Fiorese, Roberto Verardo, Fabrizio Zanconati, Maria Assunta Cova, and Nicolò de Manzini have no conflicts of interest or financial ties to disclose.

Ethical approval

The present study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data were stored anonymously in our colorectal cancer database, not open access. A proper broad informed consent for unspecified use of data was obtained for each patient. Patients’ data were stored only once anonymized, therefore confidentiality of the information linked to the data is guaranteed. Nobody among the researchers had access to the identification of patients’ information. According to local legislation (GDPR 679/2016, Par.26), anonymized data do not require data protection and ethical committee approval.

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Losurdo, P., Gandin, I., Belgrano, M. et al. microRNAs combined to radiomic features as a predictor of complete clinical response after neoadjuvant radio-chemotherapy for locally advanced rectal cancer: a preliminary study. Surg Endosc 37, 3676–3683 (2023).

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