Abstract
ObjectiveTo predict pathological nodal stage of locally advanced rectal cancer by a radiomic method that uses collective features of multiple lymph nodes (LNs) in magnetic resonance images before and after neoadjuvant chemoradiotherapy (NCRT).MethodsA total of 215 patients were included in this study and chronologically divided into the discovery cohort (n=143) and validation cohort (n=72). In total, 2,931 pre-NCRT LNs and 1,520 post-NCRT LNs were delineated from all visible rectal LNs in magnetic resonance images. Geometric, first-order and texture features were extracted from each LN before and after NCRT. Collective features are defined as the maximum, minimum, mean, median value and standard deviation of each feature from all delineated LNs of each participant. LN-model is constructed from collective LN features by logistic regression model with L1 regularization to predict pathological nodal stage (ypN0 or ypN+). Tumor-model is constructed from tumor features for comparison by using DeLong test.ResultsThe LN-model selects 7 features from 412 LN features, and the tumor-model selects 7 features from 82 tumor features. The area under the receiver operating characteristic curve (AUC) of LN-model in the discovery cohort is 0.818 [95% confidence interval (95% CI): 0.745−0.878], significantly (Z=2.09, P=0.037) larger than 0.685 (95% CI: 0.602−0.760) of the tumor-model. The AUC of LN-model in validation cohort is 0.812 (95% CI: 0.703−0.895), significantly (Z=3.106, P=0.002) larger than 0.517 (95% CI: 0.396−0.636) of the tumor-model.ConclusionsThe usage of collective features from all visible rectal LNs performs better than the usage of tumor features for the prediction of pathological nodal stage of locally advanced rectal cancer.
Highlights
Accurate prediction of pathological nodal stage may enable individualized treatments for the patients with locally advanced rectal cancer (LARC) [1,2,3,4]
[ypN0, no residual metastatic lymph nodes (LNs)] can be confirmed by radiological methods after neoadjuvant chemoradiotherapy (NCRT) and before surgery, the following treatment could be changed from total mesorectal excision (TME) into more conservative plans www.cjcrcn.org
A total of 2,931 pre-NCRT LNs and 1,520 post-NCRT LNs were delineated from 215 patients both in discovery cohort and validation cohort
Summary
Accurate prediction of pathological nodal stage may enable individualized treatments for the patients with locally advanced rectal cancer (LARC) [1,2,3,4]. The emerge of radiomics makes it possible to extract many features from images and construct a predictive model by machine learning. Several studies on colon or rectal cancer including our previous work have applied radiomics in the prediction of pathological complete response (ypT0N0), nodal stage (ypN0) or pathological good response (ypT0-1N0) [9,10,11,12,13,14,15,16]. It can be noticed that the labels in all these studies contain pathological nodal stage, but all these studies extract features only from primary tumors and no LN features are included. It is difficult to determine which LN should be used for feature extraction and classification
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