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Overall Survival of Stage III Colon Cancer with Only One Lymph Node Metastasis Is Independently Predicted by Preoperative Carcinoembryonic Antigen Level and Lymph Node Sampling Status.

ABSTRACT: This study identified predictors of favorable overall survival (OS) for stage III colon cancer patients who had only one lymph node (LN) metastasis (N1a).Variables, including preoperative carcinoembryonic antigen (CEA) level, LN sampling status, and the choices of postoperative adjuvant chemotherapy, were recorded. Prognostic significance was determined using the log-rank test and multivariate Cox regression analysis.The median 42-month follow-up period included 363 eligible patients. Among them, 230 (63.3%) received only 5-flurouracil (5-FU) adjuvant chemotherapy; 76 (20.9%) underwent oxaliplatin-based regimens; and 57 (15.7%) chose surgery alone. The 5-year survival rate of these evaluated patients was 75%, 63%, and 77%, respectively (P = 0.823). Multivariate analysis revealed that normal preoperative CEA level (?5 ng/mL) and adequate LN sampling (LN ? 12) were significant predictors for higher 5-year OS (P < 0.001; P = 0.007, respectively). However, the use of postoperative adjuvant chemotherapy in these N1a colon cancer patients did not significantly affect their 5-year OS.A preoperative CEA level of less than or equal to 5 ng/mL, and curative surgery with an adequate lymphadenectomy determined a favorable OS outcome in stage III colon cancer with only one LN metastasis.

SUBMITTER: Lin BR

PROVIDER: S-EPMC4575069 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Overall Survival of Stage III Colon Cancer with Only One Lymph Node Metastasis Is Independently Predicted by Preoperative Carcinoembryonic Antigen Level and Lymph Node Sampling Status.

Lin Been-Ren BR Lin Yu-Lin YL Lai Hong-Shiee HS Lee Po-Huang PH Chang King-Jen KJ Liang Jin-Tung JT

PloS one 20150918 9

<h4>Background</h4>This study identified predictors of favorable overall survival (OS) for stage III colon cancer patients who had only one lymph node (LN) metastasis (N1a).<h4>Methods</h4>Variables, including preoperative carcinoembryonic antigen (CEA) level, LN sampling status, and the choices of postoperative adjuvant chemotherapy, were recorded. Prognostic significance was determined using the log-rank test and multivariate Cox regression analysis.<h4>Results</h4>The median 42-month follow-u ...[more]

PMID: 26381396

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Project description:BACKGROUND:Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models. METHODS:A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). RESULTS:The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p?<?0.02) according to the DeLong method. CONCLUSIONS:The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Project description:ImportanceGuidelines recommend measuring preoperative carcinoembryonic antigen (CEA) in patients with colon cancer. Although persistently elevated CEA after surgery has been associated with increased risk for metastatic disease, prognostic significance of elevated preoperative CEA that normalized after resection is unknown.ObjectiveTo investigate whether patients with elevated preoperative CEA that normalizes after colon cancer resection have a higher risk of recurrence than patients with normal preoperative CEA.Design, setting, and participantsThis retrospective cohort analysis was conducted at a comprehensive cancer center. Consecutive patients with colon cancer who underwent curative resection for stage I to III colon adenocarcinoma at the center from January 2007 to December 2014 were identified.ExposuresPatients were grouped into 3 cohorts: normal preoperative CEA, elevated preoperative but normalized postoperative CEA, and elevated preoperative and postoperative CEA.Main outcomes and measuresThree-year recurrence-free survival (RFS) and hazard function curves over time were analyzed.ResultsA total of 1027 patients (461 [50.4%] male; median [IQR] age, 64 [53-75] years) were identified. Patients with normal preoperative CEA had 7.4% higher 3-year RFS (n = 715 [89.7%]) than the combined cohorts with elevated preoperative CEA (n = 312 [82.3%]) (P = .01) but had RFS similar to that of patients with normalized postoperative CEA (n = 142 [87.9%]) (P = .86). Patients with elevated postoperative CEA had 14.9% lower RFS (n = 57 [74.5%]) than the combined cohorts with normal postoperative CEA (n = 857 [89.4%]) (P = .001). The hazard function of recurrence for elevated postoperative CEA peaked earlier than for the other cohorts. Multivariate analyses confirmed that elevated postoperative CEA (hazard ratio [HR], 2.0; 95% CI, 1.1-3.5), but not normalized postoperative CEA (HR, 0.77; 95% CI, 0.45-1.30), was independently associated with shorter RFS.Conclusions and relevanceElevated preoperative CEA that normalizes after resection is not an indicator of poor prognosis. Routine measurement of postoperative, rather than preoperative, CEA is warranted. Patients with elevated postoperative CEA are at increased risk for recurrence, especially within the first 12 months after surgery.

Project description:BackgroundEndobronchial ultrasound-guided transbronchial fine needle aspiration (EBUS-FNA) has revolutionized the diagnostic and staging approach to non-small cell carcinoma and thoracic lymphadenopathy. However, obstacles to efficacy of rapid on-site evaluation (ROSE) of the samples include variability in sample quality and slow and cumbersome process in the procedure room leading to extended procedure time. The purpose of this pilot study was to evaluate the feasibility and specimen quality of lymph node biopsies prepared through a novel automated system for automated fixation, drying and staining compared to standard slide preparation method.MethodsWe performed a prospective, single-center pilot feasibility study of patients undergoing EBUS. Samples were split into conventional standard of care (SOC) slide preparation and preparation using the device ("instrument"). Pathologists compared the SOC slides to the slides prepared by the automated system and assessed the following metrics: nuclear and cytoplasmic quality, presence of debris/artifact, staining quality, creation of a monolayer, and ease of adequacy/diagnosis assessment. A score between 1 (lowest quality) and 3 (highest quality) was assigned to the above metrics.ResultsSixty patients were recruited. One to three lymph nodes were sampled for each patient for a total of 72 samples collected. The mean scores of each assessment category showed no statistical difference between the two preparation techniques except for improved monolayer creation in the instrument samples. Thirty of thirty-one (96.8%) paired samples in the final analysis showed diagnostic equivalency between the automated slides and conventional slides; the discordant pairing was reported to be suspicious on the instrument sample and atypical on the SOC.ConclusionsStudy results suggest that slides prepared by the automated system are of adequate quality for adequacy assessment with diagnostic concordance when compared to SOC slides.

Project description:Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Dataset Information

Overall Survival of Stage III Colon Cancer with Only One Lymph Node Metastasis Is Independently Predicted by Preoperative Carcinoembryonic Antigen Level and Lymph Node Sampling Status.

Publications

Overall Survival of Stage III Colon Cancer with Only One Lymph Node Metastasis Is Independently Predicted by Preoperative Carcinoembryonic Antigen Level and Lymph Node Sampling Status.

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