Impact of incremental circumferential resection margin distance on overall survival and recurrence in oesophageal adenocarcinoma

Knight, William; Zylstra, Janine; Wulaningsih, Wahyu; Hemelrijck, Mieke Van; Landau, David; Maisey, Nick; Gaya, A.; Baker, Cara; Gossage, James; Largergren, J.; Davies, Andrew

doi:10.1002/bjs5.65

Cited by 15 publications

(16 citation statements)

References 30 publications

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“…This cohort exhibited an early recurrence rate of 29.1%, which is similar to previous reports 3–5,8 . There was an R1 resection rate of 29.1%, in line with previously reported data 34,35 with an RCP definition of CRM positivity (CRM<1mm involved). On univariate analysis all factors expected to correlate with worse prognosis (including ypT, ypN, vascular invasion, R1 resection and grade of differentiation) were significantly worse in those patients who developed an early recurrence.…”

Section: Discussionsupporting

confidence: 90%

Machine learning to predict early recurrence after oesophageal cancer surgery

Rahman

Walker

Lloyd

et al. 2019

Preprint

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Objective: To develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multi-national cohort. Summary Background Data: Early cancer recurrence after oesophagectomy is a common problem with an incidence of 20-30% despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. Machine learning techniques potentially allow more accurate prognostication and have been applied in this study. Methods: Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in 6 UK and 1 Dutch oesophago-gastric units were analysed. Using clinical characteristics and post-operative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and XG boost (XGB). Finally, a combined (Ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model. Result: In total 812 patients were included. The recurrence rate at less than 1 year was 29.1%. All of the models demonstrated good discrimination. Internally validated AUCs were similar, with the Ensemble model performing best (ELR=0.785, RF=0.789, XGB=0.794, Ensemble=0.806). Performance was similar when using internal-external validation (validation across sites, Ensemble AUC=0.804). In the final model the most important variables were number of positive lymph nodes (25.7%) and vascular invasion (16.9%). Conclusions: The derived model using machine learning approaches and an international dataset provided excellent performance in quantifying the risk of early recurrence after surgery and will be useful in prognostication for clinicians and patients.

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Section: Discussionsupporting

confidence: 90%

Machine learning to predict early recurrence after oesophageal cancer surgery

Rahman

Walker

Lloyd

et al. 2019

Preprint

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“…Since then, CRM positivity continued to be studied, with different opinions arising about its prognostic significance, varying mainly due to the application of modern multidisciplinary management ( 9 ) and neoadjuvant therapy ( 10 , 11 ). Additionally, incremental benefits in survival and recurrence rates with increasing CRM values have been found in esophageal cancer ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

The Circumferential Resection Margin Is a Prognostic Predictor in Colon Cancer

et al. 2020

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Objective: This study aimed to investigate the potential value of circumferential resection margin (CRM) in colon cancer prognostics. Summary Background Data: CRM has been extensively studied as an important prognostic factor in rectal and esophageal cancer, but not in colon cancer. Methods: Data from 6,681 CRM-positive patients and 25,908 CRM-negative patients diagnosed with colon cancer in 2010–2015 were obtained from the Surveillance, Epidemiology, and End Results database. Statistical analysis methods utilized included the chi-square test, Kaplan-Meier estimates, Cox proportional, and X-tile software analyses. Results: After propensity score matching, CRM positivity was found to be negatively related with survival (P < 0.001). X-tile software identified 0 and 30 mm as optimal cutoff values ( P < 0.001) for prognosis, which was applicable only in stage II–IV patients. A 20 and 33% risk decrease were observed in patients with CRM between 0 and 30 mm [95% confidence interval (CI) = 0.76–0.84], and larger than 30 mm (95% CI = 0.62–0.71), respectively. Chemotherapy strongly benefited prognosis with a hazard ratio of 0.36 (95% CI = 0.34–0.38) for overall survival (OS). Patients with a CRM value of 0–30 mm seemed to benefit most from chemotherapy compared with other groups. CRM and number of regional lymph nodes are independent risk factors, and the latter is a good substitute for CRM in AJCC stage I patients. Conclusion: CRM positivity is a strong unfavorable survival indicator for colon cancer patients. A better outcome is expected with CRM values larger than 30 mm. This cutoff value only applied to stage II–IV patients. For stage I patients, number of regional lymph nodes is a good substitute to predict survival. Chemotherapy was another favorable prognostic factor, especially for patients with a CRM value between 0 and 30 mm.

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“…The impact of circumferential dissection margins, similar to periductal dissection plane, on determination of residual disease is supported by its relevance in other gastrointestinal carcinomas. The circumferential dissection margin is of prognostic significance in pancreatic carcinoma and esophageal cancer [17, 28, 29]. It seems this plane is frequently overlooked because it is a dissection plane rather than a resection plane.…”

Section: Discussionmentioning

confidence: 99%

Lost in translation: confusion on resection and dissection planes hampers the interpretation of pathology reports for perihilar cholangiocarcinoma

et al. 2019

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In perihilar cholangiocarcinoma (PHC), interpretation of the resection specimen is challenging for pathologists and clinicians alike. Thorough and correct reporting is necessary for reliable interpretation of residual disease status. The aim of this study is to assess completeness of PHC pathology reports in a single center and assess what hampers interpretation of pathology reports by clinicians. Pathology reports of patients resected for PHC at a single expert tertiary center drafted between 2000 and 2018 were assessed. Reports were assessed regarding completeness, according to the guideline of the International Collaboration on Cancer Reporting (ICCR). A total of 146 reports were assessed. Prognostic tumor characteristics such as vasoinvasive growth and perineural growth were missing in 30/146 (34%) and 22/146 (15%), respectively. One or more planes were missing in 94/146 (64%) of the reports, with the periductal dissection plane missing in 51/145 (35%). Residual disease could be re-classified from R0 to R1 in 22 patients (15%). Reasons for R1 in these patients were the presence of a positive periductal dissection plane (n = 2), < 1-mm margin at the periductal dissection plane (n = 11), or liver parenchyma (n = 9). Completeness of reports improved significantly when drafted by an expert HPB pathologist. This study demonstrates that pathology reporting of PHC is challenging. Reports are frequently incomplete and often do not incorporate assessment of all resection planes and the dissection plane. The periductal dissection plane is frequently overlooked, but is a major cause of residual disease.

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Impact of incremental circumferential resection margin distance on overall survival and recurrence in oesophageal adenocarcinoma

Cited by 15 publications

References 30 publications

Machine learning to predict early recurrence after oesophageal cancer surgery

Machine learning to predict early recurrence after oesophageal cancer surgery

The Circumferential Resection Margin Is a Prognostic Predictor in Colon Cancer

Lost in translation: confusion on resection and dissection planes hampers the interpretation of pathology reports for perihilar cholangiocarcinoma

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