Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography

D’Souza, Nigel; Shaw, Annabel; Lord, Amy; Balyasnikova, Svetlana; Abulafi, Muti; Tekkis, Paris; Brown, Gina

doi:10.1001/jamanetworkopen.2019.16987

Cited by 22 publications

(19 citation statements)

References 36 publications

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“…Pricolo et al revealed the TDs and PIN were associated with worse survival in stage III colon cancer, and they recommended a combination of TDs and lymph node examination as "N2c" (24). D'Souza et al found that the presence of tumor deposits on CT was associated with disease recurrence and had the strongest association with poor outcome in sigmoid colon cancer (25). Lino-Silva et al found tumor deposits were invariably associated with worse prognosis, especially with the increasing rate of distant metastasis (26).…”

Section: Discussionmentioning

confidence: 99%

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A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

Xiang

Ren

et al. 2021

Front. Oncol.

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ObjectivesThe present study aims to discover the risk factors of multiple metastases and develop a functional nomogram to forecast multiple metastases in metastatic colorectal cancer (mCRC) patients.MethodsmCRC cases were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Survival times between multiple metastases and single metastasis were compared using Kaplan–Meier analysis and log-rank tests. Risk factors for multiple metastases were determined by univariate and multivariate logistic regression analyses, and a nomogram was developed to forecast the probability of multiple metastases in mCRC patients. We assessed the nomogram performance in terms of discrimination and calibration, including concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). Bootstrap resampling was used as an internal verification method, and at the same time we select external data from Renmin Hospital of Wuhan University as independent validation sets.ResultsA total of 5,302 cases were included in this study as training group, while 120 cases were as validation group. The patients with single metastasis and multiple metastases were 3,531 and 1,771, respectively. The median overall survival (OS) and cancer-specific survival (CSS) for patients with multiple metastases or single metastasis were 19 vs. 31 months, and 20 vs. 33 months, respectively. Based on the univariate and multivariate analyses, clinicopathological characteristics were associated with number of metastasis and were used to establish nomograms to predict the risk of multiple metastases. The C-indexes and AUC for the forecast of multiple metastases were 0.715 (95% confidence interval (CI), 0.707–0.723), which showed the nomogram had good discrimination and calibration curves of the nomogram showed no significant bias from the reference line, indicating a good degree of calibration. In the validation group, the AUC was 0.734 (95% CI, 0.653–0.834), and calibration curve also showed no significant bias, indicating the favorable effects of our nomogram.ConclusionsWe developed a new nomogram to predict the risk of multiple metastases. The nomogram shows the good prediction effect and can provide assistance for clinical diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

“…D’Souza et al. found that the presence of tumor deposits on CT was associated with disease recurrence and had the strongest association with poor outcome in sigmoid colon cancer ( 25 ). Lino-Silva et al.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

Xiang

Ren

et al. 2021

Front. Oncol.

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“…First is the impact that careful pathological identification of TDs could have in improving colon cancer survival. There has been increasing recognition of high‐risk tumour features serving as prognostic biomarkers for outcomes in CRC, including preoperative imaging and TDs; ensuring identification may play a role in guiding treatment and outcomes [14,58,59]. TDs and PNI noted on preoperative CT scan are prognostic of a high recurrence risk and can supplement treatment planning and patient expectations [58].…”

Section: Discussionmentioning

confidence: 99%

N1c colon cancer and the use of adjuvant chemotherapy: a current audit of the National Cancer Database

et al. 2020

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Aim: Colorectal cancer staging has evolved to define N1c as the presence of tumour deposits without concurrent positive lymph nodes. Work to date reports poor prognosis in N1c colon cancer, with Stage III categorization and adjuvant chemotherapy (AC) recommended. No study has yet evaluated the prevalence, treatment compliance or treatmentrelated outcomes on a national scale. We aimed to evaluate the prevalence of N1c colon cancer, use, outcomes and factors associated with AC in the USA. Method: The National Cancer Database was reviewed for N1cM0 colon adenocarcinomas that underwent resection from 2010 to 2016. Cases were stratified into 'AC' or 'no AC' cohorts. The Kaplan-Meier method was used to estimate overall survival (OS) and compare the AC and no AC cohorts using the log-rank test. Multivariable logistic regression identified factors associated with AC. The main outcome measures were the prevalence and factors associated with AC use and its impact in N1c disease. Results: Of the 5684 (1.59% of 357 752) colon adenocarcinomas that were N1c, 55% (n = 3071) received AC. AC significantly improved 1-, 3-and 5-year OS compared with no AC (96.2%, 80%, 67.4% and 72.9%, 48.5%, 33.8%, respectively; P < 0.001). Compared with the no AC group, AC patients were younger, had less comorbidity, were of the male gender and received minimally invasive surgery at an academic treatment centre (all P < 0.05). Socioeconomic and procedural factors significantly impacted the use of AC. Conclusion: In the USA, AC is underutilized in N1c colon cancer despite significantly improved OS. Socioeconomic and procedural factors associated with AC were identified, highlighting disparities in AC use and opportunities to improve oncological outcomes and survival.

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“…Besides, preoperative neoadjuvant therapy may lead to the underestimation of EMVI status in postoperative pathological examination ( 5 ). In recent years, studies have shown that preoperative imaging can improve the prognosis of rectal cancer ( 6 ). Due to the advantages of high spatial resolution, magnetic resonance imaging (MRI) is an excellent imaging method to detect the adverse prognostic factors of rectal cancer, and it is a promising and repeatable technique for the identification of EMVI.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Liu

Yang

et al. 2021

Front. Oncol.

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ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

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Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography

Abstract: This prognostic study assesses whether preoperative computed tomographic findings can be used for sigmoid colon cancer prognosis.

Cited by 22 publications

References 36 publications

A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

N1c colon cancer and the use of adjuvant chemotherapy: a current audit of the National Cancer Database

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

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