A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer

Abstract

Objective: The aim of this study was to demonstrate the principle of supporting radiologists by using a computer algorithm to quantitatively analyse MRI morphological features used by radiologists to predict the presence or absence of metastatic disease in local lymph nodes in rectal cancer. Methods: A computer algorithm was developed to extract and quantify the following morphological features from MR images: chemical shift artefact; relative mean signal intensity; signal heterogeneity; and nodal size (volume or maximum diameter). Computed predictions on nodal involvement were generated using quantified features in isolation or in combinations. Accuracies of the predictions were assessed against a set of 43 lymph nodes, determined by radiologists as benign (20 nodes) or malignant (23 nodes). Results: Predictions using combinations of quantified features were more accurate than predictions using individual features (0.67-0.86 vs 0.58-0.77, respectively). The algorithm was more accurate when three-dimensional images were used (0.58-0.86) than when only middle image slices (two-dimensional) were used (0.47-0.72). Maximum node diameter was more accurate than node volume in representing the nodal size feature; combinations including maximum node diameter gave accuracies up to 0.91. Conclusion: We have developed a computer algorithm that can support radiologists by quantitatively analysing morphological features of lymph nodes on MRI in the context of rectal cancer nodal staging. We have shown that this algorithm can combine these quantitative indices to generate computed predictions of nodal status which closely match radiological assessment. This study provides support for the feasibility of computer-assisted reading in nodal staging, but requires further refinement and validation with larger data sets. Rectal cancer is a common and major cause of mortality in the Western world. The treatment of rectal cancer has seen significant progress over recent years with the development of new adjuvant and neoadjuvant therapies [1,2], and new surgical techniques such as transanal endoscopic microsurgery (TEM). These advances have made the accurate nodal staging of rectal cancer increasingly important in guiding the choice of treatment options for each individual patient. Confident exclusion of nodal involvement could enable patients with earlystage (T1) rectal cancer to undergo local excision such as TEM, with its advantage of reduced peri and postoperative morbidity compared with conventional surgical techniques [3][4][5]. Moreover, the accurate identification of nodes involved by tumour may identify patients who would benefit from either neoadjuvant chemoradiotherapy prior to surgery or adjuvant chemotherapy post-neoadjuvant chemoradiotherapy and resection where pre-operative treatment prevents accurate identification of pre-treatment disease status. Currently, rectal cancer staging is performed with MRI, which has established itself as the reference standard [6][7][8].Different techniques have been evaluated to address th...

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

“…Finally, 17 studies were included in the systematic review, 12 of which could be used in the meta-analysis and five studies were excluded due to insufficient information (Fig. 1 ) [ 11 , 12 , 20 – 34 ].

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Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis

“…These algorithms capture the image texture and morphology of tumors based on their gray values. Since 2018, many reports on radiological methods for rectal cancer lymph node assessment have been published (75)(76)(77)(78). However, when analyzing imaging information and building predictive models, all these parameters require time-consuming calculations.…”

Intra- and Interobserver Variability in Magnetic Resonance Imaging Measurements in Rectal Cancer Patients

“…A recent study by Ding et al suggested that artificial intelligence (AI) might add diagnostic accuracy in the evaluation of metastatic LNs in patients with rectal cancer [21]. Other methods are also being tested, and there is a need for further improvement in the LN staging of rectal cancer [22][23][24][25][26][27][28][29][30][31].…”