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Prediction of Lymph Node Status in Superficial Esophageal Carcinoma

  • Gastrointestinal Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

Esophageal carcinoma is among the cancers with the worst prognosis. Real chances for cure depend on both early recognition and early treatment. The ability to predict lymph node involvement allows early curative treatment with less invasive approaches.

Aims

To determine clinicohistopathological criteria correlated with lymph node involvement in patients with early esophageal cancer (T1) and to identify the best candidate patients for local endoscopic or less invasive surgical treatments.

Methods

A total of 98 patients with pT1 esophageal cancer [67 with squamous cell carcinomas (SCC) and 31 with adenocarcinomas (ADK)] underwent Ivor–Lewis or McKeown esophagectomy in the period between 1980 and 2006 at our institution. Based on the depth of invasion, lesions were classified as m1, m2, or m3 if mucosal, and sm1, sm2, or sm3 if submucosal.

Results

The rates of lymph node metastasis were 0% for the 27 mucosal carcinomas (T1m) and 28% for the 71 submucosal (T1sm) carcinomas (< 0.001). Sm1 carcinomas were associated with a lower rate of lymph-node metastasis (8.3% versus 49 % sm2/3,  = 0.003). As for histotype, the rates of lymph node metastasis for sm1 were 0% for ADK and 12.5% for SCC; for sm2/3 there were no significant differences. On multivariate analysis, depth of infiltration, lymphocytic infiltrate, angiolymphatic and neural invasion were significantly associated with lymph node involvement. Neural invasion was the single parameter with the greatest accuracy (82%); depth of infiltration and angiolymphatic invasion had 75% accuracy. Altogether these three parameters had an accuracy of 97%. Five-year survival rate was 56.7% overall: 77.7% for T1m and 53.3% for T1sm ( = 0.048).

Conclusions

The most important factors for predicting lymph node metastasis in early esophageal cancer are depth of tumor infiltration, angiolymphatic invasion, neural invasion and grade of lymphocytic infiltration. The best candidates for endoscopic therapy are tumors with high-grade lymphocytic infiltration, no angiolymphatic or neural invasion, mucosal infiltration or sm1 (only for ADK), and tumor <1 cm in size. For sm SCC and sm2/3 ADK the treatment of choice remains esophagectomy with standard lymphadenectomy.

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Correspondence to Ermanno Ancona MD, FACS.

Appendix

Appendix

Exclusion of Angiolymphatic Invasion in Our Logistic Regression. The Causes

Angiolymphatic invasion—the parameter which most significantly correlated with lymph node metastases at univariate analysis ( < 0.0001)—could not be included in the logistic regression analysis because it had a null frequency.

In our logistic regression, the dependent variable y is binary and takes a value of 1 or 0 if there is presence or absence of lymph node metastases.

Let us consider only one explanatory variable x that takes a value of 1 or 0 if there is presence or absence of angiolymphatic invasion.

Then the model is:

$$ P(Y = 1|X = x) = \pi (x) = \frac{{\exp (\beta _{0} + \beta _{1} x)}} {{1 + \exp (\beta _{0} + \beta _{1} x)}}. $$

Let odds(x) be defined as the ratio

$$ {\text{odds}}(x) = \frac{{P(Y = 1|X = x)}} {{P(Y = 0|X = x)}} = \frac{{P(Y = 1|X = x)}} {{1 - P(Y = 1|X = x)}} = \frac{{\pi (x)}} {{1 - \pi (x)}}. $$

The log of the odds (logit) is a linear function of the explanatory variable

$$ {\text{logit}}(x) = \ln [{\text{odds}}(x)] = \beta _{0} + \beta _{1} x. $$

If X and Y are binary variables, the sample could be represented by a contingency table like Table A1 the estimated probabilities are

$$ \hat{\pi }(0) = \frac{b} {{a + b}}, $$
$$ \hat{\pi }(1) = \frac{d} {{c + d}} $$
Table A1 Contingency table for two binary variables: general case

and the estimated odds are

$$ {\text{odds}}(1) = \frac{{\pi (1)}} {{1 - \pi (1)}} = \frac{b} {a}, $$
$$ {\text{odds}}(0) = \frac{{\pi (0)}} {{1 - \pi (0)}} = \frac{d} {c}. $$

Let us now consider our values (Table A2): the estimated probabilities are

$$ \hat{\pi }(0) = \frac{{20}} {{40}} = 0.5, $$
$$ \hat{\pi }(1) = \frac{{50}} {{50}} = 1 $$
Table A2 Contingency table for two binary variables: our data

and the estimated odds are

$$ {\text{odds}}(0) = \frac{{\pi (1)}} {{1 - \pi (1)}} = 1, $$
$$ {\text{odds}}(1) = \frac{{\pi (0)}} {{1 - \pi (0)}} = \frac{1} {{1 - 0}} = \frac{1} {0} = {\text{impossible}}! $$

The calculation of odds(1) is not possible and so we could not include angiolymphatic invasion in the logistic regression.

Exclusion of Angiolymphatic Invasion in Our Logistic Regression. The Solution

Angiolymphatic invasion is correlated with grading ( < 0.0001) and growth pattern ( = 0.0004), and as such, on multivariate analysis, the variability explained by angiolymphatic invasion could be partially included in the contributions of these two parameters.

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Ancona, E., Rampado, S., Cassaro, M. et al. Prediction of Lymph Node Status in Superficial Esophageal Carcinoma. Ann Surg Oncol 15, 3278–3288 (2008). https://doi.org/10.1245/s10434-008-0065-1

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  • DOI: https://doi.org/10.1245/s10434-008-0065-1

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