Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information

Sa, Sha; Li, Jing; Li, Yongrui; Liu, Xiaoming; Wang, Defeng; Zhang, Huimao; Fu, Yu

doi:10.18632/oncotarget.19427

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The explanation might be that the linear and nodular patterns are associated with deeper infiltration, resulting in more vascular invasion and LN metastasis. Previous studies reported that pericolic fat infiltration was correlated with the pathological variables of CRC 12,36,37 . A study by Kim et al 12 reported that PD colorectal adenocarcinoma demonstrated significantly more nodular pericolic fat infiltration than WD or MD.…”

Section: Discussionmentioning

confidence: 92%

“…A study by Kim et al 12 reported that PD colorectal adenocarcinoma demonstrated significantly more nodular pericolic fat infiltration than WD or MD. Sa et al 36 analyzed the correlation between the T stage of CRC and nine CT imaging characteristics. Six CT variables, including pericolic fat infiltration, were positively correlated with the T stage.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

Lu,

Sun,

Wang

et al. 2024

J. Cancer

View full text Add to dashboard Cite

Objective: To establish a nomogram prediction model (based on clinicopathological and radiological features) for the development of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). Methods: This retrospective study included patients with CRC who underwent surgery at Changshu No.1 People's Hospital and the Second Affiliated Hospital of Soochow University between January 2016 and December 2018. The clinical, pathological, and radiological features of each patient were investigated. Risk factors for MLM were identified by univariable and multivariable analyses. The predictive nomogram for MLM development was constructed. The predictive performance of the nomogram was estimated by the receiver operating characteristics curve, calibration curve, and decision curve analysis. Results: This study included 161 patients with CRC [median age: 66 (range, 33-87) years]. Fifty-nine developed MLM after a median of 12 (range, 2-52) months after surgery. The multivariable logistic regression analysis showed that age >66 years (OR=3.471, 95% CI: 1.272-9.473, P =0.015), N2 stage (OR=6.534, 95% CI: 1.456-29.317, P =0.014), positive vascular invasion (OR=2.995, 95% CI: 1.132-7.926, P =0.027), positive tumor deposit (OR=4.451, 95% CI: 1.153-17.179, P =0.030), and linear (OR=6.774, 95% CI: 1.306-35.135, P =0.023) and nodal pericolic fat infiltration patterns (OR=8.762, 95% CI: 1.521-50.457, P =0.015) were independently associated with MLM. These five factors were used to create a nomogram. The area under the receiver operating characteristics curve of the nomogram was 0.866 (95% CI: 0.803-0.914), indicating favorable prediction performance. The calibration curve of the nomogram showed a satisfactory agreement between the predicted and actual probabilities. Conclusions: A nomogram prediction model based on five clinicopathological and radiological features might have favorable prediction performance for MLM in patients who underwent surgery for CRC. Hence, the present study proposes a nomogram that can easily be used to predict MLM after CRC surgery based on readily available features.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

Lu,

Sun,

Wang

et al. 2024

J. Cancer

View full text Add to dashboard Cite

show abstract

“…Existing studies have proved that CT images can be applied to CRC assessments. Sha S used CT images to predict the T‐staging of colorectal cancer, and Huang Y presented a prognostic framework for lymph node metastasis in colorectal cancer based on CT images. Overall, CT‐based studies have the potential to improve the diagnosis and treatment of CRC.…”

Section: Introductionmentioning

confidence: 99%

Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network

et al. 2019

Self Cite

View full text Add to dashboard Cite

Purpose Colorectal tumor segmentation is an important step in the analysis and diagnosis of colorectal cancer. This task is a time consuming one since it is often performed manually by radiologists. This paper presents an automatic postprocessing module to refine the segmentation of deep networks. The label assignment generative adversarial network (LAGAN) is improved from the generative adversarial network (GAN) and assigns labels to the outputs of deep networks. We apply the LAGAN to segment colorectal tumors in computed tomography (CT) scans and explore the performances of different combinations of deep networks. Material and methods A total of 223 patients with colorectal cancer (CRC) are enrolled in the study. The CT scans of the colorectal tumors are first segmented by FCN32 and Unet separately, which output probabilistic maps. Then, the probabilistic maps are labeled by the LAGAN and finally, the binary segmentation results are obtained. The LAGAN consists of a generating model and a discriminating model. The generating model utilizes the probabilistic maps from deep networks to imitate the distribution of the ground truths, and the discriminating model attempts to distinguish generations and ground truths. Through competitive training, the generating model of the LAGAN can realize label assignments for the probabilistic maps. Results The LAGAN increases the DSC of FCN32 from 81.83% ± 0.35% to 90.82% ± 0.36%. In the Unet‐based segmentation, the LAGAN increases the DSC from 86.67% ± 0.70% to 91.54% ± 0.53%. It takes approximately 10 ms to refine a single CT slice. Conclusions The results demonstrate that the LAGAN is a robust and flexible module, which can be used to refine the segmentation of diverse deep networks. Compared with other networks, the LAGAN can achieve desirable segmented accuracy for colorectal tumors.

show abstract

The effect of lymph node ratio on the surgical outcomes in patients with colorectal cancer

Hai,

Peng,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The current study aimed to evaluate the effect of lymph node ratio (LNR) on the short-term and long-term outcomes of colorectal cancer (CRC) patients who underwent radical CRC surgery. We retrospectively collected CRC patients who underwent radical surgery from Jan 2011 to Jan 2020 in a single-center hospital. The patients were divided into the high LNR group and the low group according to the median. The baseline information and the short-term outcomes were compared between the high group and the low group. Univariate and multivariate logistic regression was performed to analyze the independent predictors for overall survival (OS) and disease-free survival (DFS). A 1:1 proportional propensity score matching (PSM) was used to reduce the selection bias between the two groups. Kaplan–Meier method was used to estimate the OS and DFS between the two groups in different T stages. A total of 1434 CRC patients undergoing radical surgery were enrolled in this study, and there were 730 (50.9%) patients in the low LNR group and 704 (49.1%) patients in the high LNR group. After the PSM, there were 618 patients in both groups, the baseline characteristics between the two groups had no significant difference ( p > 0.05). After comparing the Surgery-related information and The Short-term outcomes, the high LNR group had a longer hospital stay (after PSM, p < 0.01). In univariate and multivariate logistic regression analyses, age (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor location (univariate analysis, p = 0.020; multivariate analysis, p = 0.024), lymph-vascular space invasion (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), cancer nodules (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor size (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), LNR (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), and overall complications (univariate analysis, p < 0.01; multivariate analysis, p < 0.01) were independent risk factors for OS, and age (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), tumor location (univariate analysis, p = 0.032; multivariate analysis, p = 0.031), T stage (univariate analysis, p < 0.01; multivariate analysis, p = 0.014), lymph-vascular space invasion (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), cancer nodules (univariate analysis, p < 0.01; multivariate analysis, p < 0.01), LNR (univariat...

show abstract

Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information

Cited by 5 publications

References 28 publications

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network

The effect of lymph node ratio on the surgical outcomes in patients with colorectal cancer

Contact Info

Product

Resources

About