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A New Surveillance Algorithm After Resection of Colorectal Liver Metastases Based on Changes in Recurrence Risk and RAS Mutation Status

Yoshikuni Kawaguchi, Scott Kopetz, Heather A. Lillemoe, Hyunsoo Hwang, Xuemei Wang, Ching-Wei D. Tzeng, Yun Shin Chun, Thomas A. Aloia, and Jean-Nicolas Vauthey

Background: The optimal surveillance strategy after resection of colorectal liver metastases (CLM) is unknown. We evaluated changes in recurrence risk after CLM resection and developed a surveillance algorithm. Methods: Patients undergoing CLM resection during 1998 to 2015 were identified from a prospectively compiled database and analyzed if they had the potential for follow-up longer than the longest observed time to recurrence in this cohort. Changes in recurrence risk and risk factors for recurrence were evaluated. All statistical tests were 2-sided. Results: Among 2,105 patients who were initially identified and underwent CLM resection, the latest recurrence was observed at 87 months; 1,221 consecutive patients from 1998 through 2011 with the potential for at least 87 months of follow-up were included. The risk of recurrence was highest at 0 to 2 years after CLM resection, lower at 2 to 4 years after CLM resection, and steadily lower after 4 years after CLM resection. Factors associated with increased recurrence risk at the time of surgery were primary lymph node metastasis (hazard ratio [HR], 1.54; 95% CI, 1.21–1.97; P<.001), multiple CLM (HR, 1.31; 95% CI, 1.06–1.63; P=.015), largest liver metastasis diameter >5 cm (HR, 1.64; 95% CI, 1.23–2.19; P<.001), and RAS mutation (HR, 1.29; 95% CI, 1.04–1.59; P=.020). In patients without recurrence at 2 years, the only factor still associated with increased recurrence risk was RAS mutation. In those patients, the recurrence rate at 4 years was 59.3% in patients with RAS mutation versus 27.8% in patients with RAS wild-type (P=.019). Conclusions: For patients who have undergone CLM resection, we propose surveillance every 3 to 4 months during years 0 to 2, every 3 to 4 months (if mutant RAS) versus every 4 to 6 months (if RAS wild-type) during years 2 to 4, and every 6 to 12 months if recurrence-free at 4 years.

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A Nomogram to Predict Distant Metastases After Multimodality Therapy for Patients With Localized Esophageal Cancer

Kazuki Sudo, Xuemei Wang, Lianchun Xiao, Roopma Wadhwa, Hironori Shiozaki, Elena Elimova, David C. Rice, Jeffrey H. Lee, Brian Weston, Manoop S. Bhutani, Adarsh Hiremath, Nikolaos Charalampakis, Ritsuko Komaki, Mariela A. Blum, Stephen G. Swisher, Dipen M. Maru, Heath D. Skinner, Jeana L. Garris, Jane E. Rogers, Wayne L. Hofstetter, and Jaffer A. Ajani

Background: Among patients with localized esophageal cancer (LEC), 35% or more develop distant metastases (DM) as first relapse, most in the first 24 months after local therapy. Implementation of novel strategies may be possible if DM can be predicted reliably. We hypothesized that clinical variables could help generate a DM nomogram. Patients and Methods: Patients with LEC who completed multimodality therapy were analyzed. Various statistical methods were used, including multivariate analysis to generate a nomogram. A concordance index (c-index) was established and validated using the bootstrap method. Results: Among 629 patients analyzed (356 trimodality/273 bimodality), 36% patients developed DM as first relapse. The median overall survival from DM was only 8.6 months (95% CI, 7.0–10.2). In a multivariate analysis, the variables associated with a higher risk for developing DM were poorly differentiated histology (hazard ratio [HR], 1.76; P<.0001), baseline T3/T4 primary (HR, 3.07; P=.0006), and baseline N+ LEC (HR, 2.01; P<.0001). Although variables associated with a lower risk for DM were age of 60 years or older (HR, 0.75; P=.04), squamous cell carcinoma (HR, 0.54; P=.013), and trimodality therapy (HR, 0.58; P=.0001), the bias-corrected c-index was 0.67 after 250 bootstrap resamples. Conclusions: Our nomogram identified patients with LEC who developed DM with a high probability. The model needs to be refined (tumor and blood biomarkers) and validated. This type of model will allow implementation of novel strategies in patients with LEC.