Dataset Information

Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.

ABSTRACT:

Objective

This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images.

Background

nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty.

Methods

842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning.

Results

The proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively).

Conclusions

The DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.

SUBMITTER: Lou X

PROVIDER: S-EPMC9214314 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPatients with locally advanced rectal cancer (LARC) have an improved prognosis if achieved a pathological complete response (pCR) on account of neoadjuvant chemoradiation therapy (nCRT). However, the proportion of patients achieving pCR is only 8-24%. The purpose of this study was to explore whether the addition of consolidation chemotherapy to nCRT could improve pCR rate in patients with LARC.Materials and methodsThe subjects were 144 individuals with clinical stage II (T3-4, N0) or III (any T, N1-2) LARC who had received neoadjuvant CRT followed by total mesorectal excision (TME). Eighty-three patients in the consolidation chemotherapy group received two cycles XELOX between CRT and TME, while 61 patients in the standard treatment group without consolidation chemotherapy. The pCR (ypT0N0), tumor downstaging (ypT0-2N0) after TME and adverse events (AEs) during and post treatment were compared between the treatment groups using multivariable logistic regression analysis. To adjust the unbalanced variables for the primary endpoint, logistic regression analysis and stratified analysis were performed.ResultsThe consolidation chemotherapy group improved pCR rate (19.3% vs 4.9%, p = 0.01) and tumor downstaging rate (45.8% vs 24.6%, p = 0.009) compared to the standard treatment group. After adjustment for clinical tumor stage, clinical nodal stage and time interval to surgery, patients with consolidation chemotherapy were more likely to reach pCR (adjusted odds ratio 4.91, 95% CI [1.01-23.79], p = 0.048). AEs during and post treatment in the two groups were 54.1% vs 49.3% (p = 0.57), respectively. In addition, the incidence of any grade 1-2 AEs in the two groups was 93.4% vs 95.1% (p = 0.93), while the incidence of grade 3 AEs was 1.6% versus 2.4% (p = 0.74), respectively. No grade 4 AEs occurred in two groups.ConclusionsThe addition of neoadjuvant consolidation chemotherapy after CRT significantly increased the pCR rate and did not increase the AEs during and post treatment and in patients with LARC.

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