Development and validation of a prognostic nomogram for colorectal cancer after surgery

BACKGROUNDA nomogram is a diagram that aggregates various predictive factors through multivariate regression analysis, which can be used to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions are two critical factors that affect the prognosis of patients with colorectal cancer (CRC) after surgery. At present, few effective tools have been established to predict the overall survival (OS) of CRC patients after surgery.AIMTo screen out suitable risk factors and to develop a nomogram that predicts the postoperative OS of CRC patients.METHODSData from a total of 3139 patients diagnosed with CRC who underwent surgical removal of tumors and LN resection from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results program. The data were divided into a training set (n = 2092) and a validation set (n = 1047) at random. The Harrell concordance index (C-index), Akaike information criterion (AIC), and area under the curve (AUC) were used to assess the predictive performance of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio (LNR), and log odds of positive lymph nodes (LODDS). Univariate and multivariate analyses were utilized to screen out the risk factors significantly correlating with OS. The construction of the nomogram was based on Cox regression analysis. The C-index, receiver operating characteristic (ROC) curve, and calibration curve were employed to evaluate the discrimination and prediction abilities of the model. The likelihood ratio test was used to compare the sensitivity and specificity of the final model to the model with the N stage alone to evaluate LN metastasis.RESULTSThe predictive efficacy of the LODDS was better than that of the LNR based on the C-index, AIC values, and AUC values of the ROC curve. Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. Great consistency between the predicted and actual observed OS for the 1-, 3-, and 5-year OS in the training set and validation set was shown in the calibration curves. The final nomogram showed a better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P < 0.001) through the likelihood ratio test.CONCLUSIONThe nomogram incorporating LODDS, TD, and other risk factors showed great predictive accuracy and better sensitivity and specificity and represents a potential tool for therapeutic decision-making.     

个体化预测腹腔镜胃癌术后小肠梗阻的列线图模型的建立

目的探讨腹腔镜胃癌术后小肠梗阻的危险因素,并建立预测腹腔镜胃癌术后小肠梗阻的列线图模型。方法选取2017年2月-2020年12月进入淮南东方医院集团总院进行诊治的258例行腹腔镜手术的胃癌患者作为研究对象,采用Logistic回归分析筛选腹腔镜胃癌术后小肠梗阻的危险因素,采用R(R3.5.3)建立预测腹腔镜胃癌术后小肠梗阻的风险列线图模型。结果所选258例行腹腔镜手术的胃癌患者中有39例患者术后发生小肠梗阻,小肠梗阻的发生率为15.12%(39/258)。Logistic回归分析结果显示,性别(女)、年龄≥60岁、腹部手术史、BMI>24 kg/m²、糖尿病及手术医生经验≤5年是腹腔镜胃癌术后小肠梗阻的危险因素(P<0.05)。列线图模型结果显示,模型一致性指数(C-index)为0.795(95%CI:0.749~0.842)校正曲线与理想曲线基本一致,受试者工作特征曲线(receiver operating characteristic curve,ROC)曲线下面积(AUC)为0.781,决策曲线显示阈值概率在3%~83%时,具有较高的净获益值。结论性别、年龄≥60岁、腹部手术史、BMI>24 kg/m²、糖尿病及手术医生经验≤5年是腹腔镜胃癌术后小肠梗阻的危险因素,基于危险因素建立的列线图有助于预测腹腔镜胃癌术后小肠梗阻的发生风险。     

Prediction of prolonged resolution of chylous ascites after radical D3 resection for colorectal cancer: A population-based experience from a high-volume center

AimThis study was aimed to analyze the incidence, risk factors, and management of chylous ascites (CA) after radical D3 resection for colorectal cancer, and to construct a predicting nomogram for prolonged resolution of CA.MethodConsecutive colorectal cancer patients who underwent radical D3 resection were included. Logistic analysis was used to identify risk factors of postoperative CA, as well as prolonged CA resolution. A predictive nomogram for prolonged resolution of CA was developed and validated internally.ResultsAmong 7167 patients included, 277 (3.8%) patients developed CA. Logistic regression analysis demonstrated that laparoscopic operation (OR 1.507; P = 0.017) and tumors fed by the superior mesenteric artery (SMA, OR 2.456; P < 0.001) were independent risk factors of postoperative CA following radical D3 surgery for colorectal cancer. Open operation (OR 0.422; P = 0.027), drainage output on the first day of treatment (OR 1.004; P = 0.016), time to oral intake (OR 1.273; P = 0.042), and time to onset (OR 1.231; P = 0.024) were independently associated with prolonged resolution of postoperative CA (≥7 days). A predictive nomogram for prolonged CA resolution was developed with a C-index of 0.725.ConclusionThe incidence of CA after radical D3 surgery of colorectal cancer was 3.8%. Open operation, drainage output on the first day of treatment, time to oral intake, and time to onset were independently associated with prolonged resolution of postoperative CA. A nomogram may assist in tailored treatment decision-making and counseling patient with treatment strategies.     

Maximizing the clinical usefulness of a nomogram to select patients candidate to sentinel node biopsy for cutaneous melanoma

Aims

Investigators from the Memorial Sloan Kettering Cancer Centre (MSKCC) have proposed a nomogram for predicting the sentinel node (SN) status in patients with cutaneous melanoma. The negative predictive value (NPV) of this test, which might help identify low-risk patients who might be safely spared SN biopsy (SNB), has not been yet investigated.

Methods

We tested the discrimination (area under the curve [AUC]), the calibration (linear regression) and the NPV of MSKCC nomogram in 543 patients treated at our institution. Different cut-off values were tested to assess the NPV, the reduction of SNB performed and the overall error rate obtained with the MSKCC nomogram.

Results

SN was positive in 147 patients (27%). Mean predicted probability was 17.8% (95%CI: 16.8-18.8%). Nomogram discrimination was significant (area under the curve = 0.68; P < 0.0001) and mean predicted probabilities of SN positivity well correlated with the observed risk (R² = 0.99). Cut-off values between 4% and 9% led to a NPV, SNB reduction and overall error rates ranging between 100 and 91.2%, 2.2 and 27.2%, and 0 and 2.3%, respectively.

Conclusion

In our series, the nomogram showed a significant predictive accuracy, although the incidence of SN metastasis was higher than that observed in the MSKCC series (27% vs 16%). Using the nomogram, a NPV greater than 90% could be obtained, which would be associated with a clinically meaningful reduction of the SNB rate and an acceptable error rate. If validated in large prospective series, this tool might be implemented in the clinical setting for SNB patient selection.     

External Validation of Urinary PCA3-Based Nomograms to Individually Predict Prostate Biopsy Outcome

Background

Prior to safely adopting risk stratification tools, their performance must be tested in an external patient cohort.

Objective

To assess accuracy and generalizability of previously reported, internally validated, prebiopsy prostate cancer antigen 3 (PCA3) gene-based nomograms when applied to a large, external, European cohort of men at risk of prostate cancer (PCa).

Design, setting, and participants

Biopsy data, including urinary PCA3 score, were available for 621 men at risk of PCa who were participating in a European multi-institutional study.

Intervention

All patients underwent a ≥10-core prostate biopsy. Biopsy indication was based on suspicious digital rectal examination, persistently elevated prostate-specific antigen level (2.5–10 ng/ml) and/or suspicious histology (atypical small acinar proliferation of the prostate, >/= two cores affected by high-grade prostatic intraepithelial neoplasia in first set of biopsies).

Measurements

PCA3 scores were assessed using the Progensa assay (Gen-Probe Inc, San Diego, CA, USA). According to the previously reported nomograms, different PCA3 score codings were used. The probability of a positive biopsy was calculated using previously published logistic regression coefficients. Predicted outcomes were compared to the actual biopsy results. Accuracy was calculated using the area under the curve as a measure of discrimination; calibration was explored graphically.

Results and limitations

Biopsy-confirmed PCa was detected in 255 (41.1%) men. Median PCA3 score of biopsy-negative versus biopsy-positive men was 20 versus 48 in the total cohort, 17 versus 47 at initial biopsy, and 37 versus 53 at repeat biopsy (all p ≤ 0.002). External validation of all four previously reported PCA3-based nomograms demonstrated equally high accuracy (0.73–0.75) and excellent calibration. The main limitations of the study reside in its early detection setting, referral scenario, and participation of only tertiary-care centers.

Conclusions

In accordance with the original publication, previously developed PCA3-based nomograms achieved high accuracy and sufficient calibration. These novel nomograms represent robust tools and are thus generalizable to European men at risk of harboring PCa. Consequently, in presence of a PCA3 score, these nomograms may be safely used to assist clinicians when prostate biopsy is contemplated.     

A preoperative prognostic model for patients treated with nephrectomy for renal cell carcinoma

Background

Currently two pretreatment prognostic models with limited accuracy (65–67%) can be used to predict survival in patients with localized renal cell carcinoma (RCC).

Objective

We set out to develop a more accurate pretreatment model for predicting RCC-specific mortality after nephrectomy for all stages of RCC.

Design, setting, and participants

The data originated from a series of prospectively recorded contemporary cases of patients treated with radical or partial nephrectomy between 1984 and 2006. Model development was performed using data from 2474 patients from five centers and external validation was performed using data from 1972 patients from seven centers.

Measurements

The probability of RCC-specific mortality was modeled using Cox regression. The significance of the predictors was confirmed using competing risks analyses, which account for mortality from other causes.

Results and limitations

Median follow-up in patients who did not die of RCC-specific causes was 4.2 yr and 3.5 yr in the development and validation cohorts, respectively. The freedom from cancer-specific mortality rates in the nomogram development cohort were 75.4% at 5 yr after nephrectomy and 68.3% at 10 yr after nephrectomy. All variables except gender achieved independent predictor status. In the external validation cohort the nomogram predictions were 88.1% accurate at 1 yr, 86.8% accurate at 2 yr, 86.8% accurate at 5 yr, and 84.2% accurate at 10 yr.

Conclusions

Our model substantially exceeds the accuracy of the existing pretreatment models. Consequently, the proposed nomogram-based predictions may be used as benchmark data for pretreatment decision making in patients with various stages of RCC.