EPD402 - Artificial Intelligence–Assisted Identification Of Residual/Recurrent Disease In Patients With Locally Advanced Rectal Cancer With Complete Clinical Response To Neoadjuvant Chemoradiotherapy

Monday, May 11, 2026

1:36 PM - 1:41 PM ET

ePoster of Distinction Presenter(s)

MM

Miguel Mascarenhas, n/a

Gastroenterology, Centro Hospitalar Universitario de Sao Joao
Hospital São João, Porto.
Porto, Porto, Portugal

All disclosed relationships have been reviewed and managed prior to the start of this activity. Any relevant relationships will be listed below. This individual has no financial relationships to disclose

Author(s)

MC

Matheus Carvalho, MD

Dr
Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo de Ribeirão Preto
São Paulo, Sao Paulo, Brazil

Disclosure information not submitted.
FM

Francisco Mendes, .

Department of Gastroenterology, São João University Hospital
.
., Porto, Portugal

Disclosure information not submitted.
AS

André Santos, MD

Dr
Faculdade de Medicina da Universidade do Porto
Porto, Porto, Portugal

Disclosure information not submitted.
MJ

Maria João Almeida, n/a

Department of Gastroenterology, São João University Hospital
.
., Porto, Portugal

Disclosure information not submitted.
JM

Joana Mota, n/a

Dr
Hospital São João, Porto.
Porto, Porto, Portugal

Disclosure information not submitted.
JA

João Afonso, n/a

Dr
Hospital São João, Porto.
Porto, Porto, Portugal

Disclosure information not submitted.
TR

Tiago Ribeiro, n/a

Department of Surgery, Instituto de Infectologia Emílio Ribas
.
., Porto, Portugal

Disclosure information not submitted.
MM

Miguel Martins, n/a

Engineer
Faculty of Engineering of the University of Porto
Porto, Porto, Portugal

Disclosure information not submitted.
AB

Abdullah Bunaiyan, n/a

Dr
Royal Liverpool University Hospital, Liverpool
liverpool, England, United Kingdom

Disclosure information not submitted.
Sw

S. willoughby, n/a

Dr
Liverpool University Hospitals NHS Foundation Trust
Liverpool, England, United Kingdom

Disclosure information not submitted.
RR

R Rajan, n/a

Dr
Liverpool University Hospitals NHS Foundation Trust
Liverpool, England, United Kingdom

Disclosure information not submitted.
EC

Eduardo Carvalho, n/a

Mr
Institute of Science and Innovation in Mechanical and Industrial Engineering
Porto, Porto, Portugal

Disclosure information not submitted.
SA

S Ahmed, MD

Dr.
Liverpool University Hospitals NHS Foundation Trust
Liverpool, England, United Kingdom

Disclosure information not submitted.
GB

Guilherme Barbosa, MD

Dr
INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal,
Porto, Porto, Portugal

Disclosure information not submitted.
NR

Nilza Ramião, n/a

Ms
Institute of Science and Innovation in Mechanical and Industrial Engineering
Porto, Porto, Portugal

Disclosure information not submitted.
ML

Marcos Lera dos Santos, n/a

Dr
Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo de Ribeirão Preto
São Paulo, Sao Paulo, Brazil

Disclosure information not submitted.
EM

Eduardo Guimarães Moura, PhD

Dr
Hospital São Luiz Rede D'or
São Paulo, Sao Paulo, Brazil

Disclosure information not submitted.
GM

Guilherme Macedo, .

Department of Gastroenterology, São João University Hospital
.
., Porto, Portugal

Disclosure information not submitted.

Senior Author(s)

AJ

Ahsan Javed, n/a

Dr
Liverpool University Hospitals NHS Foundation Trust
Liver, England, United Kingdom

Disclosure information not submitted.

Author(s)

PC

Pedro Cardoso, MD

Dr
Centro Hospitalar Universitário de São João, Gastroenterology, Porto, Portugal
Porto, Porto, Portugal

Disclosure information not submitted.
AP

Antonio Pinto da Costa, .

Department of Gastroenterology, Puerta del Hierro University Hospital
.
., Porto, Portugal

Disclosure information not submitted.

Purpose/Background: Patients with locally advanced rectal cancer (LARC) who have a complete clinical response (cCR) to nCRT may opt for organ preservation, watch & wait – (W&W). This consists of an intense surveillance program including serial endoscopies, pelvic MRI, CEA, and CT scans to detect recurrent disease at an early stage. However, identifying residual or recurrent lesions endoscopically - particularly in patients with prior radiotherapy - can be challenging due to mucosal changes such as friability and neovascularization. In this context, we developed a novel deep learning model to assist in the detection of residual or recurrent rectal cancer lesions during proctosigmoidoscopy.

Methods/Interventions: We trained a convolutional neural network (Wide ResNet-101-2) on a dataset of 1,795 annotated frames from proctosigmoidoscopy exams of 97 patients treated at University Hospitals Liverpool, a tertiary referral center. Residual or recurrent disease was defined by histopathological confirmation via biopsy or surgical specimen. The dataset was split into training and testing cohorts using a 90/10% patient-level split. The model’s diagnostic performance was evaluated using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUROC).

Results/Outcomes: Out of 97 patients, 24 (363 frames) had confirmed residual or recurrent disease, while 73 (1,432 frames) presented normal
rectal mucosa. The model achieved an overall accuracy of 92.8%, with a sensitivity of 80.0%, specificity of 97.3%, PPV of 90.9%, NPV
of 94.0%, and an AUROC of 0.886.

Conclusion/Discussion: To the best of our knowledge, this is the first deep learning model specifically developed for the detection of residual or recurrent disease following in W&W patients during endoscopic examination. This tool has the potential to enhance early lesion detection, guide clinical decision-making, and increase opportunities for organ-preserving, less invasive therapeutic strategies.