Development and external validation of a nomogram for overall survival after curative resection in serosa-negative, locally advanced gastric cancer

CT-based scores for extramural vascular invasion and occult peritoneal metastasis correlate with gastric cancer survival

OBJECTIVE

To assess the feasibility of scoring extragastric vascular invasion and occult peritoneal metastasis using preoperative computed tomography (CT) images of gastric cancer (GC) and to explore the correlation between these scores and patient prognosis.

METHODS

587 GC patients with CT scans from two centers, all confirmed by pathology, were retrospectively evaluated. Scores for CT-detected blood vessel invasion (ctBVI), lymphatic invasion (ctLVI), and occult peritoneal metastasis (ctOPM) were assigned based on preoperative CT images. The patients' follow-up provided data on overall and disease-free survival. Cox proportional hazard models were used to analyze prognostic factors.

RESULTS

The inter-group and intra-group consistency of ctBVL, ctLVI, and ctOPM scores were all > 0.70. Log-rank analysis demonstrated a statistically significant difference in survival curves (p < 0.001). CtBVL, ctLVI, and ctOPM scores were related to overall survival (OS) and disease-free survival (DFS). Univariate and multivariate Cox regression analyses identified ctBVL, ctLVI, ctOPM scores as independent risk factors for GC prognosis. In multivariate analysis, the three sign scores were related to DFS (p < 0.05), with ctBVL (hazard ratio (HR) = 1.980, 95% CI: 1.336-2.933), ctLVI (HR = 1.502, 95% CI: 1.336-2.933), and ctOPM (HR = 1.182, 95% CI: 0.886-1.578). The three scores were also correlated with OS (p < 0.05), ctBVL (HR = 2.003, 95% CI: 1.278-3.139), ctLVI (HR = 1.523, 95% CI:1.055-2.200) and ctOPM (HR = 1.289, 95% CI: 1.013-1.770).

CONCLUSION

CtBVL, ctLVI, and ctOPM scores are valuable prognostic indicators in gastric cancer, influencing both OS and DFS.

KEY POINTS

Question To study whether the ctBVL, ctLVI, and ctOPM scores assessed by preoperative enhanced CT imaging can predict the survival outcomes of patients. Findings CtBVL, ctLVI, and ctOPM scores, assessed via preoperative enhanced CT imaging, are associated with worse survival outcomes when elevated. Clinical relevance CtBVL, ctLVI, and ctOPM scores may help guide personalized follow-up plans. Patients with higher scores might require closer monitoring and more aggressive treatment.

Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymphovascular invasion in gastric cancer

BACKGROUND

To develop and validate a dual-phase contrast-enhanced computed tomography (CT)-based intratumoral and peritumoral radiomics for the prediction of lymphovascular invasion (LVI) in patients with gastric cancer.

METHOD

Three hundred and eighty-three patients with gastric cancer (training cohort, 269 patients; test cohort, 114 patients) were retrospectively enrolled between January 2017 and June 2023. Radiomics features were extracted from the intratumoral volume (ITV) and peritumoral volume (PTV) on CT images at arterial phase (AP) and venous phase (VP), and selected by the least absolute shrinkage and selection operator. Radiomics models were constructed by logistic regression. The clinical-radiomics combined model incorporating the most predictive radiomics signature and clinical risk factors were developed with multivariate analysis. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of models.

RESULTS

Clinical model comprised of three clinical risk factors including tumor differentiation, CT-reported lymph node metastasis status and CT-TNM staging showed good performance with an area under the ROC curve (AUC) of 0.804 and 0.825 in the training and test cohort, respectively. Compared with the other radiomics models, dual-phase (AP + VP) CT-based ITV + PTV radiomics model presented superior AUC of 0.844 and 0.835 in the training and test cohort, respectively. Clinical-radiomics combined model further improved the discriminatory performance (AUC, 0.903) in the training and test cohort (AUC, 0.901). Decision curve analysis confirmed the net benefit of clinical-radiomics combined model. Subgroup analyses showed that the clinical-radiomics nomogram showed the best performance with an AUC of 0.879 and 0.883 for predicting LVI in T1-T2 and T3-T4 gastric cancer compared with the clinical model and the ITV + PTV-AP + VP radiomics model, respectively.

CONCLUSIONS

Clinical-radiomics combined model integrating clinical risk factors and dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics signatures provided favorable performance for predicting LVI in gastric cancer.

A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer

The objective of the present study was to build and validate a radio-clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast-enhanced computed tomography within one month before surgery. The focal area was mapped by ITK-SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio-clinical model was constructed by employing multi-variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio-clinical model, consisting of rad-score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio-clinical model. In conclusion, the radio-clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.

Prognostic significance of a pathological response in metastatic lymph nodes of patients with gastric cancer who underwent neoadjuvant chemotherapy followed by surgery

PURPOSE

To grade the pathological response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) and investigate its prognostic significance.

METHODS

This retrospective study included 196 patients who underwent NAC, followed by radical gastrectomy for LAGC between January 2010 and October 2019. Pathological responses were evaluated based on the proportion of residual tumor cells within the tumor area in the primary tumor (PT) and LNs and included the following categories: 1a (0%), 1b (< 10%), 2 (10-50%), and 3 (> 50%).

RESULTS

Among 166 patients with clinically node-positive disease, 38/27/39/62 were classified as having LN regression grade (LRG) 1a/1b/2/3, respectively. Compared to LN non-responders (LRG 2 or 3), LN responders (LRG 1a or 1b) had significantly higher 5-year overall survival (72.5% vs. 19.0%, P < 0.001) and recurrence-free survival rates (67.8% vs. 22.2%, P < 0.001), irrespective of PT response. Furthermore, a multivariate analysis revealed that the LN response was an independent risk factor for the overall survival (hazard ratio [HR] 0.417, 95% confidence interval [CI] 0.181-0.962, P = 0.040) and recurrence-free survival (HR 0.490, 95% CI 0.242-0.991, P = 0.047), but not the PT response (P > 0.05).

CONCLUSIONS

The pathological LN response may be a reliable prognostic prediction tool in patients with LAGC who received NAC.

Nomogram for predicting overall survival after curative gastrectomy using inflammatory, nutritional and pathological factors

PURPOSE

To establish a nomogram for predicting the overall survival (OS) in patients with gastric cancer (GC) based on inflammatory, nutritional and pathological factors.

METHODS

GC patients underwent curative gastrectomy from January 2012 to June 2017 in our hospital were included, and were classified into training set and validation set with a ratio of 7:3. Then variables associated with OS were analyzed using univariate and multivariate Cox regression analysis. Nomograms predicting OS were built using variables from multivariable Cox models. Finally, Kaplan-Meier curve and Log-rank test were also conducted to analyze the 1-yr, 3-yr and 5-yr OS to validate the efficiency of risk stratification of the nomogram.

RESULTS

A total of 366 GC patients were included. After univariate and multivariate Cox regression analysis, age (HR = 1.52, 95% CI = 1.01-2.30, P = 0.044), CA50 (HR = 1.90, 95% CI = 1.12-3.21, P = 0.017), PNI (HR = 1.65, 95% CI = 1.13-2.39, P = 0.009), SII (HR = 1.46, 95% CI = 1.03-2.08, P = 0.036), T stage (HR = 2.26, 95% CI = 1.01-5.05, P = 0.048; HR = 7.24, 95% CI = 3.64-14.40, P < 0.001) were independent influencing factors on the survival time of GC patients. Five factors including CEA, prognostic nutritional index (PNI), systemic immune-inflammation index (SII), ln (tumor size), T stage, and N stage were identified and entered the nomogram, which showed good discrimination and calibration in both sets. On internal validation, 1-yr, 3-yr and 5-yr nomogram demonstrated a good discrimination with an area under the ROC curve (AUC) of 0.77, 0.84 and 0.86, respectively. The AUC for 1-yr, 3-yr and 5-yr nomogram in validation set was 0.77, 0.79 and 0.81, respectively. The OS in low risk group of training cohort and validation cohort was significantly higher than that of intermediate risk group and high risk group, respectively.

CONCLUSIONS

We established a nomogram based on PNI, SII and pathological factors for predicting OS in GC patients. In addition, its efficiency was validated by validation set and stratified analysis.

Abnormal arginine synthesis confers worse prognosis in patients with middle third gastric cancer

BACKGROUND

Gastric cancer at different locations has distinct prognoses and biological behaviors, but the specific mechanism is unclear.

METHODS

Non-targeted metabolomics was performed to examine the differential metabolite phenotypes that may be associated with the effects of tumor location on the prognosis of gastric cancer. And silencing of the rate-limiting enzyme to evaluate the effect of abnormal changes in metabolic pathway on the functional biological assays of gastric cancer cells HGC-27 and MKN28.

RESULTS

In a retrospective study of 94 gastric cancer patients, the average survival time of patients with gastric cancer in the middle third of the stomach was significantly lower than that of patients with gastric cancer in other locations (p < 0.05). The middle third location was also found to be an independent risk factor for poor prognosis (HR = 2.723, 95%CI 1.334-5.520), which was closely associated with larger tumors in this location. Non-targeted metabolomic analysis showed that the differential metabolites affected 16 signaling pathways including arginine synthesis, retrograde endocannabinoid signaling, arginine biosynthesis, and alanine and aspartate and glutamate metabolism between gastric cancer and normal tissue, as well as between tumors located in the middle third of the stomach and other locations. Argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme of the arginine biosynthesis pathway, catalyzes the production of argininosuccinic acid. Here, knockdown of ASS1 significantly inhibited the proliferation, colony formation, and migration/invasion of gastric cancer cells, and promoted apoptosis.

CONCLUSIONS

Our study suggests that abnormal arginine synthesis may lead to larger tumor size and worse prognosis in gastric cancer located in the middle third position of the stomach. These findings may provide the basis for the stratification and targeted treatment of gastric cancer in different locations.

Application of intravoxel incoherent motion diffusion-weighted imaging for preoperative knowledge of lymphovascular invasion in gastric cancer: a prospective study

PURPOSE

To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC).

METHODS

This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman's correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy.

RESULTS

The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05).

CONCLUSION

The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy.

The GLIM criteria as an effective tool for survival prediction in gastric cancer patients

BACKGROUND

The Global Leadership Initiative on Malnutrition released a new version of the malnutrition criteria (GLIM criteria). To investigate the influence of the GLIM criteria on the long-term efficacy of radical gastric cancer surgery and establish a nomogram to predict the long-term prognosis of patients with gastric cancer.

METHODS

A retrospective analysis of 1121 patients with gastric cancer in our department from 2010 to 2013 was performed. A nomogram was established to predict overall survival (OS) based on the GLIM criteria. Patients were divided into the low-risk group (LRG) and high-risk group (HRG) based on the established nomogram.

RESULTS

Multivariate Cox regression analyses showed that GLIM criteria was an independent risk factor for the 5-year OS (HR = 1.768, Cl:1.341-2.329, p < 0.001). The C index, AUC and Time-ROC of the nomogram were significantly better than that of GLIM criteria and traditional criteria. The 5-year OS of patients receiving adjuvant chemotherapy in the high-risk group was significantly higher than that of patients without chemotherapy (45.77% vs. 24.73%,p < 0.001).

CONCLUSIONS

The GLIM criteria independently influence the long-term outcome of patients after radical gastric cancer surgery. The established nomogram can predict the long-term survival of patients with gastric cancer, and postoperative adjuvant chemotherapy for HRG can significantly improve the 5-year OS of patients.

Nomogram to Predict Recurrence and Guide a Pragmatic Surveillance Strategy After Resection of Hepatoid Adenocarcinoma of the Stomach: A Retrospective Multicenter Study

BACKGROUND

An accurate recurrence risk assessment system and surveillance strategy for hepatoid adenocarcinoma of the stomach (HAS) remain poorly defined. This study aimed to develop a nomogram to predict postoperative recurrence of HAS and guide individually tailored surveillance strategies.

METHODS

The study enrolled all patients with primary HAS who had undergone curative-intent resection at 14 institutions from 2004 to 2019. Clinicopathologic variables with statistical significance in the multivariate Cox regression were incorporated into a nomogram to build a recurrence predictive model.

RESULTS

The nomogram of recurrence-free survival (RFS) based on independent prognostic factors, including age, preoperative carcinoembryonic antigen, number of examined lymph nodes, perineural invasion, and lymph node ratio, achieved a C-index of 0.723 (95% confidence interval [CI], 0.674-0.772) in the whole cohort, which was significantly higher than those of the eighth American Joint Committed on Cancer (AJCC) staging system (C-index, 0.629; 95% CI, 0.573-0.685; P < 0.001). The nomogram accurately stratified patients into low-, middle-, and high-risk groups of postoperative recurrence. The postoperative recurrence risk rates for patients in the middle- and high-risk groups were respectively 3 and 10 times higher than for the low-risk group. The patients in the middle- and high-risk groups showed more recurrence and metastasis, particularly multiple site metastasis, within 36 months after the operation than those in the low-risk group (low, 2.2%; middle, 8.6%; high, 24.0%; P = 0.003).

CONCLUSIONS

The nomogram achieved good prediction of postoperative recurrence for the patients with HAS after radical resection. For the middle- and high-risk patients, more active surveillance and targeted examination methods should be adopted within 36 months after the operation, particularly for liver and multiple metastases.

Nomogram model predicting the overall survival for patients with primary gastric mucosa-associated lymphoid tissue lymphoma

BACKGROUND

Increasingly extranodal marginal B-cell lymphoma of mucosa-associated lymphoid tissue, known as mucosa-associated lymphoid tissue (MALT) lymphoma, is a type of non-Hodgkin's lymphoma. The prognosis of primary gastric MALT (GML) patients can be affected by many factors. Clinical risk factors, including age, type of therapy, sex, stage and family hematologic malignancy history, also have significant effects on the development of the disease. The available data are mainly focused on epidemiology; in contrast, few studies have investigated the prognostic variables for overall survival (OS) in patients with primary GML. Based on the realities above, we searched a large amount of data on patients diagnosed with primary GML in the Surveillance, Epidemiology and End Results (SEER) database. The aim was to develop and verify a survival nomogram model that can predict the overall survival prognosis of primary GML by combining prognostic and determinant variables.

AIM

To create an effective survival nomogram for patients with primary gastric GML.

METHODS

All data of patients with primary GML from 2004 to 2015 were collected from the SEER database. The primary endpoint was OS. Based on the LASSO and COX regression, we created and further verified the accuracy and effectiveness of the survival nomogram model by the concordance index (C-index), calibration curve and time-dependent receiver operating characteristic (td-ROC) curves.

RESULTS

A total of 2604 patients diagnosed with primary GML were selected for this study. A total of 1823 and 781 people were randomly distributed into the training and testing sets at a ratio of 7:3. The median follow-up of all patients was 71 mo, and the 3- and 5-year OS rates were 87.2% and 79.8%, respectively. Age, sex, race, Ann Arbor stage and radiation were independent risk factors for OS of primary GML (all P < 0.05). The C-index values of the nomogram were 0.751 (95%CI: 0.729-0.773) and 0.718 (95%CI: 0.680-0.757) in the training and testing cohorts, respectively, showing the good discrimination ability of the nomogram model. Td-ROC curves and calibration plots also indicated satisfactory predictive power and good agreement of the model. Overall, the nomogram shows favorable performance in discriminating and predicting the OS of patients with primary GML.

CONCLUSION

A nomogram was developed and validated to have good survival predictive performance based on five clinical independent risk factors for OS for patients with primary GML. Nomograms are a low-cost and convenient clinical tool in assessing individualized prognosis and treatment for patients with primary GML.

Nomogram-Based Prognostic Evaluation of Gastric Cancer Patients with Low Counts of Examined Lymph Nodes Outperforms the Predictive Ability of the 7th and 8th Editions of the American Joint Committee on Cancer Staging System

BACKGROUND

The American Joint Committee on Cancer (AJCC) staging system has limited accuracy in predicting survival of gastric cancer patients with inadequate counts of evaluated lymph nodes (LNs). We therefore aimed to develop a prognostic nomogram suitable for clinical applications in such cases.

METHODS

A total of 1511 noncardia gastric cancer patients treated between 1990 and 2010 in the academic surgical center were reviewed to compare the 7^th and 8^th editions of the AJCC staging system. A nomogram was developed for the prediction of 5-year survival in patients with less than 16 LNs evaluated (n = 546). External validation was performed using datasets derived from the Polish Gastric Cancer Study Group (n = 668) and the SEER database (n = 11,225).

RESULTS

The 8th edition of AJCC staging showed better overall discriminatory power compared to the previous version, but no improvement was found for patients with < 16 evaluated LNs. The developed nomogram had better concordance index (0.695) than the former (0.682) or latest (0.680) staging editions, including patients subject to neoadjuvant treatment, and calibration curves showed excellent agreement between the nomogram-predicted and actual survival. High discriminatory power was also demonstrated for both validation cohorts. Subsequently, the nomogram showed the best accuracy for the prediction of 5-year survival through the time-dependent ROC curve analysis in the training and validation cohorts.

CONCLUSIONS

A clinically relevant nomogram was built for the prediction of 5-year survival in patients with inadequate numbers of LNs evaluated in surgical specimens. The predictive accuracy of the nomogram was validated in two Western populations.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic

BACKGROUND

The survival benefit of primary and metastatic resection for patients with colorectal cancer with liver metastasis (CRLM) has been observed, but methods for discriminating which individuals would benefit from surgery have been poorly defined. Herein, a predictive model was developed to stratify patients into sub-population based on their response to surgery.

METHODS

We assessed the survival benefits for adults diagnosed with colorectal liver metastasis by comparing patients with curative surgery vs. those without surgery. CRLM patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction. Other data including CRLM patients from our center were obtained for external validation. Calibration plots, the area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with the tumor-node-metastasis (TNM) classification. The Kaplan-Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery.

RESULTS

A total of 1,220 eligible patients were identified, and 881 (72.2%) underwent colorectal and liver resection. Cancer-specific survival (CSS) for the surgery group was significantly better than that for the no-surgery group (41 vs. 14 months, p < 0.001). Five factors were found associated with CSS and adopted to build the nomograms, i.e., age, T stage, N stage, neoadjuvant chemotherapy, and primary tumor position. The AUC of the CRLM nomogram showed a better performance in identifying patients who could obtain benefits in the surgical treatment, compared with TNM classification (training set, 0.826 [95% CI, 0.786-0.866] vs. 0.649 [95% CI, 0.598-0.701]; internal validation set, 0.820 [95% CI, 0.741-0.899] vs. 0.635 [95% CI, 0.539-0.731]; external validation set, 0.763 [95% CI, 0.691-0.836] vs. 0.626 [95% CI, 0.542-0.710]). The calibration curves revealed excellent agreement between the predicted and actual survival outcomes. The DCA showed that the nomogram exhibited more clinical benefits than the TNM staging system. The beneficial and surgery group survived longer significantly than the non-beneficial and surgery group (HR = 0.21, 95% CI, 0.17-0.27, p < 0.001), but no difference was observed between the non-beneficial and surgery and non-surgery groups (HR = 0.89, 95% CI, 0.71-1.13, p = 0.344).

CONCLUSIONS

An accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for the resection of primary and metastatic lesions among CRLM patients.

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Objectives

To develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC.

Methods

The two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA).

Results

Tumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (P<0.001). It yielded an AUC of 0.825 (95% confidence interval [95% CI], 0.769-0.872) and 0.802 (95% CI, 0.716-0.871) in primary and validation datasets (all P<0.05), with promising clinical utility by DCA.

Conclusion

This study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.

An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts

BACKGROUND

Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC.

METHODS

Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation.

RESULTS

In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients.

CONCLUSIONS

The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.

Very Early Recurrence After Curative-Intent Surgery for Gastric Adenocarcinoma

BACKGROUND

Recurrence after curative-intent surgery can occur in more than 50% of gastric cancer (GC) patients. We sought to identify predictors of very early recurrence (VER) among GC patients who underwent curative-intent surgery.

METHODS

A multi-institutional database of GC patients undergoing curative-intent surgery between 2000 and 2020 at 8 major institutions was queried. VER was defined as local or distant tumor recurrence within 6 months from surgery. Univariable Cox proportional hazard models were used to evaluate the predictive value of clinical-pathological features on VER. A regularized Cox regression model was employed to build a predictive model of VER and recurrence within 12 months. The discriminant ability of the Cox regularized models was evaluated by reporting a ROC curve together with the calibration plot, considering 200 runs.

RESULTS

Among 1133 patients, 65 (16.0%) patients experienced a VER. Preoperative symptoms (HR 1.198), comorbidities (HR 1.289), tumor grade (HR 1.043), LNR (HR 4.339) and T stage (HR 1.639) were associated with an increased likelihood of VER. Model performance was very good at predicting VER at 6 months (AUC of 0.722) and 12 months (AUC 0.733). Two nomograms to predict 6-month and 12-month VER were built based on the predictive model. A higher nomogram score was associated with worse prognosis. There was good prediction between observed and estimated VER with minimal evidence of overfitting and good performance on internal bootstrapping validation.

CONCLUSION

One in 6 patients experienced VER following curative-intent surgery for GC. Nomograms to predict risk of VER performed well on internal validation, and stratified patients into distinct prognostic groups relative to 6- and 12-months recurrence.

Validation of the Memorial Sloan Kettering Gastric Cancer Post-Resection Survival Nomogram: Does It Stand the Test of Time?

BACKGROUND

The Memorial Sloan Kettering Cancer Center (MSK) nomogram combined both gastroesophageal junction (GEJ) and gastric cancer patients and was created in an era from patients who generally did not receive neoadjuvant chemotherapy. We sought to reevaluate the MSK nomogram in the era of multidisciplinary treatment for GEJ and gastric cancer.

STUDY DESIGN

Using data on patients who underwent R0 resection for GEJ or gastric cancer between 2002 and 2016, the C-index of prediction for disease-specific survival (DSS) was compared between the MSK nomogram and the American Joint Committee on Cancer (AJCC) 8th edition staging system after segregating patients by tumor location (GEJ or gastric cancer) and neoadjuvant treatment. A new nomogram was created for the group for which both systems poorly predicted prognosis.

RESULTS

During the study period, 886 patients (645 gastric and 241 GEJ cancer) underwent up-front surgery, and 999 patients (323 gastric and 676 GEJ) received neoadjuvant treatment. Compared with the AJCC staging system, the MSK nomogram demonstrated a comparable C-index in gastric cancer patients undergoing up-front surgery (0.786 vs 0.753) and a better C-index in gastric cancer patients receiving neoadjuvant treatment (0.796 vs 0.698). In GEJ cancer patients receiving neoadjuvant chemotherapy, neither the MSK nomogram nor the AJCC staging system performed well (C-indices 0.647 and 0.646). A new GEJ nomogram was created based on multivariable Cox regression analysis and was validated with a C-index of 0.718.

CONCLUSIONS

The MSK gastric cancer nomogram's predictive accuracy remains high. We developed a new GEJ nomogram that can effectively predict DSS in patients receiving neoadjuvant treatment.

Standardizing the classification of gastric cancer patients with limited and adequate number of retrieved lymph nodes: an externally validated approach using real-world data

BACKGROUND

Currently, there is no formal consensus regarding a standard classification for gastric cancer (GC) patients with < 16 retrieved lymph nodes (rLNs). Here, this study aimed to validate a practical lymph node (LN) staging strategy to homogenize the nodal classification of GC cohorts comprising of both < 16 (Limited set) and ≥ 16 (Adequate set) rLNs.

METHODS

All patients in this study underwent R0 gastrectomy. The overall survival (OS) difference between the Limited and Adequate set from a large Chinese multicenter dataset was analyzed. Using the 8th American Joint Committee on Cancer (AJCC) pathological nodal classification (pN) for GC as base, a modified nodal classification (N') resembling similar analogy as the 8th AJCC pN classification was developed. The performance of the proposed and 8th AJCC GC subgroups was compared and validated using the Surveillance, Epidemiology, and End Results (SEER) dataset comprising of 10,208 multi-ethnic GC cases.

RESULTS

Significant difference in OS between the Limited and Adequate set (corresponding N0-N3a) using the 8th AJCC system was observed but the OS of N0limited vs. N1adequate, N1limited vs. N2adequate, N2limited vs. N3aadequate, and N3alimited vs. N3badequate subgroups was almost similar in the Chinese dataset. Therefore, we formulated an N' classification whereby only the nodal subgroups of the Limited set, except for pT1N0M0 cases as they underwent less extensive surgeries (D1 or D1 + gastrectomy), were re-classified to one higher nodal subgroup, while those of the Adequate set remained unchanged (N'0 = N0adequate + pT1N0M0limited, N'1 = N1adequate + N0limited (excluding pT1N0M0limited), N'2 = N2adequate + N1limited, N'3a = N3aadequate + N2limited, and N'3b = N3badequate + N3alimited). This N' classification demonstrated less heterogeneity in OS between the Limited and Adequate subgroups. Further analyses demonstrated superior statistical performance of the pTN'M system over the 8th AJCC edition and was successfully validated using the SEER dataset.

CONCLUSION

The proposed nodal staging strategy was successfully validated in large multi-ethnic GC datasets and represents a practical approach for homogenizing the classification of GC cohorts comprising of patients with < 16 and ≥ 16 rLNs.

Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging

OBJECTIVES

Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging.

METHODS

A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included. Based on CECT imaging, we built two gastric imaging (GI) markers, GI-marker-1 from radiomics and GI-marker-2 from DTL features, to decode LVI status. We then integrated demographics, clinical data, GI markers, radiologic interpretation, and biopsies into a Gastric Cancer Risk (GRISK) model for predicting LVI. The performance of GRISK model was tested and applied to predict survival outcomes in GC patients. Furthermore, the prognosis between LVI (+) and LVI (-) patients was compared in chemotherapy and non-chemotherapy cohorts, respectively.

RESULTS

GI-marker-1 and GI-marker-2 yield similar performance in predicting LVI in training and testing dataset. The GRISK model yields the diagnostic performance with AUC of 0.755 (95% CI 0.719-0.790) and 0.725 (95% CI 0.669-0.781) in training and testing dataset. Patients with LVI (+) trend toward lower progression-free survival (PFS) and overall survival (OS). The difference of prognosis between LVI (+) and LVI (-) was more noticeable in non-chemotherapy than that in chemotherapy group.

CONCLUSION

Radiomics and deep transfer learning features on CECT demonstrate potential power for predicting LVI in GC patients. Prospective use of a GRISK model can help to optimize individualized treatment decisions and predict survival outcomes.

Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Development of a Nomogram in Breast Cancer Patients

Purpose

The objective of this study was to explore the prognostic significance of pretreatment hematologic parameters in predicting disease-free survival (DFS) of breast cancer patients.

Materials and Methods

The medical records of 440 breast cancer patients in Shandong Cancer Hospital and Institute from 2003 to 2013 were analyzed retrospectively. Through the results of blood routine before treatment, the absolute lymphocyte count (ALC), absolute neutrophil count (ANC), absolute monocyte count (AMC), and absolute platelet count (APC) in peripheral blood were collected. The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-monocyte ratio (NMR) were calculated. Cox proportional hazard model was used for univariate and multivariate analysis. The DFS was compared using Kaplan–Meier method. The prognostic nomogram of patients with breast cancer was developed.

Results

The median DFS for all patients was 64.10 months. Univariate analysis showed that the DFS was associated with surgical approach, TNM stage, molecular subtype, neoadjuvant chemotherapy, radiotherapy, and LMR (p < 0.05). TNM stage, molecular subtype, and LMR were independent prognostic factors of breast cancer in multivariate analysis (p < 0.05). According to the Kaplan–Meier survival curve analysis, patients with higher LMR (≥4.85) were associated with longer median DFS (median DFS, 85.83 vs. 60.90, p < 0.001). The proposed nomogram that incorporated LMR, TNM stage, and molecular subtype got a concordance index (c-index) of 0.69 in predicting 5-year DFS.

Conclusion

In breast cancer patients, higher LMR was associated with longer median DFS and the nomogram including LMR, TNM stage, and molecular subtype could accurately predict the prolonged 5-year DFS of breast cancer patients.

Dynamic Prediction of Survival after Curative Resection of Gastric Adenocarcinoma: A landmarking-based analysis

BACKGROUND

Accurate estimation of survival and recurrence are important to inform decisions regarding therapy and surveillance. We sought to design and validate a dynamic prognostic model for patients undergoing resection for gastric adenocarcinoma.

METHODS

Patients who underwent curative-intent surgery for gastric adenocarcinoma between 2000 and 2020 were identified using a multi-institutional database. Landmark analysis was used to create dynamic OS and DFS prediction models. Model performance was internally cross-validated via bootstrap resampling.

RESULTS

Among 895 patients, 507 (57.2%) patients underwent partial gastrectomy (n = 507, 57.2%) while 380 (42.8%) had total gastrectomy. Median tumor size was 40 mm (IQR: 25-65), most tumors were located in the antrum (n = 344, 39.5%) and infiltrated the subserosa (T3 tumors: n = 283, 31.9%) or serosa (T4 tumors: n = 253, 28.5%); lymph node metastasis occurred in 528 (59.1%) patients. Median OS and DFS were 17.5 (IQR: 7.5-42.8) and 14.3 months (IQR: 6.5-39.9), respectively. The impact of age, sex, preoperative comorbidities, tumor size and location, extent of lymphadenectomy and total number of lymph nodes examined, Lauren class, T and N category, postoperative complications, and tumor recurrence varied over time (all p < 0.05). An online tool to predict dynamic OS and DFS based on patient survival relative to time survived was developed and made available for clinical use. Discrimination ability of OS and DFS was excellent (C-index: 0.84 and 0.86, respectively) and calibration plots revealed good prediction.

CONCLUSIONS

An online dynamic prognostic tool was developed and validated to predict OS and DFS following resection of gastric adenocarcinoma. Landmark analysis to predict long-term outcomes based on follow-up time may be helpful to surgeons and patients.

Validation of a Nomogram to Predict Long Term Outcomes After Curative Surgery for Gastric Cancer in an Italian Cohort of Patients

AIM OF THE STUDY

Nomograms have been proposed to assess prognosis following curative surgery for gastric cancer. The objective of the current study was to evaluate the performance of the Gastric Cancer Collaborative Group nomograms developed in 2014 by Kim et al., using a cohort of patients from a 10-year single institution experience in gastric cancer management.

PATIENTS AND METHODS

We retrospectively reviewed patients who underwent curative-intent surgery for histologically confirmed gastric cancer at First Surgical Clinic of Padua University Hospital (Italy) from January 2010 to May 2020. Univariable and multivariable Cox proportional hazard models were employed to assess the effect of the variables of interest on mortality and recurrence. Multivariable analysis was performed by considering the variables included in the Gastric Cancer Collaborative Group nomograms in order to validate them. The performance of the nomograms was evaluated using Harrell's C-index and calibration plots.

RESULTS

Overall, 168 patients were included, with a median follow-up of 20.1 months. On multivariable analysis, tumor location, lymph node ratio, and pathological T stage were associated with recurrence; age, tumor location, lymph node ratio, and pT stage were associated with OS (overall survival). The nomograms had good discriminatory capability to classify both OS (C-index: 0.75) and DFS (disease-free survival) (C-index 0.72). The corrected C-Index for DFS based on the AJCC staging system revealed better prediction (C-Index 0.75), while the corrected C-Index for OS had worse discrimination ability compared with the current nomogram (C-Index 0.72).

CONCLUSIONS

The Gastric Cancer Collaborative Group nomograms demonstrated good performances in terms of prediction of both OS and DFS on external validation. The two nomograms are easy to apply, and variables included are widely available to most facilities.

Development and validation of pretreatment nomogram for disease-specific mortality in gastric cancer-A competing risk analysis

Background

In several reports, gastric cancer nomograms for predicting overall or disease‐specific survival have been described. The American Joint Committee on Cancer (AJCC) introduced the attractiveness of disease‐specific mortality (DSM) as an endpoint of risk model. This study aimed to develop the first pretreatment gastric cancer nomogram for predicting DSM that considers competing risks (CRs).

Methods

The prediction model was developed using data for 5231 gastric cancer patients. Fifteen prognosticators, which were registered at diagnosis, were evaluated. The nomogram for DSM was created as visualizations of the multivariable Fine and Gray regression model. An independent cohort for external validation consisted of 389 gastric cancer patients from a different institution. The performance of the model was assessed by discrimination (Harrell's concordance (C)‐index), calibration, and decision curve analysis. DSM and CRs were evaluated, paying special attention to host‐related factors such as age and Eastern Cooperative Oncology Group performance status (ECOG PS), by using Gray's univariable method.

Results

Fourteen prognostic factors were selected to develop the nomogram. The new nomogram for DSM exhibited good discrimination. Its C‐index of 0.887 surpassed that of the American Joint Committee on Cancer (AJCC) clinical staging (0.794). The C‐index was 0.713 (AJCC, 0.582) for the external validation cohort. The nomogram showed good performance internally and externally, in the calibration and decision curve analysis. Host‐related factors including age and ECOG PS, were strongly correlated with competing risks.

Conclusions

The newly developed nomogram accurately predicts DSM, which can be used for patient counseling in clinical practice.

A nomogram to predict overall survival and disease-free survival after curative-intent gastrectomy for gastric cancer

An individual prediction of DFS and OS may be useful after surgery for gastric cancer to inform patients and to guide the clinical management. Patients who underwent curative-intent resection for gastric cancer between January 2010 and May 2020 at a single Italian institution were identified. Variables associated with OS and DFS were recorded and analysed according to univariable and multivariable Cox models. Nomograms predicting OS and DFS were built according to variables resulting from multivariable Cox models. Discrimination ability was calculated using the Harrell's Concordance Index. Overall, 168 patients underwent curative-intent resection. Nomograms to predict OS were developed including age, tumor size, tumor location, T stage, N stage, M stage and post-operative complications, while nomogram to predict DFS includes Lauren classification, and lymph node ratio (LNR). On internal validation, both nomograms demonstrated a good discrimination with a Harrell's C-index of 0.77 for OS and 0.71 for DFS. The proposed nomogram to predict DFS and OS after curative-intent surgery for gastric cancer showed a good discrimination on internal validation, and may be useful to guide clinician decision-making, as well help identify patients with high-risk of recurrence or with a poor estimated survival.

Development of a Prognostic Nomogram and Nomogram Software Application Tool to Predict Overall Survival and Disease-Free Survival After Curative-Intent Gastrectomy for Gastric Cancer

BACKGROUND

We sought to derive and validate a prediction model of survival and recurrence among Western patients undergoing resection of gastric cancer.

METHODS

Patients who underwent curative-intent surgery for gastric cancer at seven US institutions and a major Italian center from 2000 to 2020 were included. Variables included in the multivariable Cox models were identified using an automated model selection procedure based on an algorithm. Best models were selected using the Bayesian information criterion (BIC). The performance of the models was internally cross-validated via the bootstrap resampling procedure. Discrimination was evaluated using the Harrell's Concordance Index and accuracy was evaluated using calibration plots. Nomograms were made available as online tools.

RESULTS

Overall, 895 patients met inclusion criteria. Age (hazard ratio [HR] 1.47, 95% confidence interval [CI] 1.17-1.84), presence of preoperative comorbidities (HR 1.66, 95% CI 1.14-2.41), lymph node ratio (LNR; HR 1.72, 95% CI 1.42-2.01), and lymphovascular invasion (HR 1.81, 95% CI 1.33-2.45) were associated with overall survival (OS; all p < 0.01), whereas tumor location (HR 1.93, 95% CI 1.23-3.02), T category (Tis-T1 vs. T3: HR 0.31, 95% CI 0.14-0.66), LNR (HR 1.82, 95% CI 1.45-2.28), and lymphovascular invasion (HR 1.49; 95% CI 1.01-2.22) were associated with disease-free survival (DFS; all p < 0.05) The models demonstrated good discrimination on internal validation relative to OS (C-index 0.70) and DFS (C-index 0.74).

CONCLUSIONS

A web-based nomograms to predict OS and DFS among gastric cancer patients following resection demonstrated good accuracy and discrimination and good performance on internal validation.

Development and evaluation of a ceMDCT-based preoperative risk stratification model to predict disease-free survival after radical surgery in patients with gastric cancer

PURPOSE

To develop and evaluate a preoperative risk stratification model for predicting disease-free survival (DFS) based on contrast-enhanced multidetector computed tomography (ceMDCT) images in patients with gastric cancer (GC) undergoing radical surgery.

METHODS

We retrospectively enrolled patients with GC who underwent ceMDCT followed by radical surgery. A preoperative risk stratification model was constructed (including risk factor selection, risk status scoring, and risk level assignment) using Cox proportional hazard regression and log-rank analyses in the training cohort; the model was tested in the validation cohort. A nomogram was used to compare the preoperative risk stratification model with a postoperative DFS prediction model.

RESULTS

A total of 462 patients (training/validation: 271/191) were included. The ceMDCT features of T category (score of 0 or 2), N category (0, 1, 2, or 3), extramural vessel invasion (0 or 2), and tumor location (0 or 1) were selected to construct the preoperative risk stratification model, with 4 risk levels defined based on risk score. There were significant differences in DFS among the risk levels in both cohorts (p < 0.001). The predictive value of the preoperative model was similar to that of the postoperative model, with concordance indices of 0.791 (95% CI, 0.743-0.837) and 0.739 (95% CI, 0.666-0.812), respectively, in the training cohort and 0.762 (95% CI, 0.696-0.828) and 0.738 (95% CI, 0.684-0.792), respectively, in the validation cohort.

CONCLUSION

A preoperative risk stratification model based on ceMDCT images could be used to predict DFS and thus classify GC cases into various risk levels.

A novel nomogram based on cardia invasion and chemotherapy to predict postoperative overall survival of gastric cancer patients

Background

We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection.

Methods

A total of 6543 patients diagnosed with primary GC during 2004–2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We grouped patients diagnosed during 2004–2012 into a training set (n = 4528) and those diagnosed during 2013–2016 into an external validation set (n = 2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot.

Results

The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia invasion was 0.762 (P < 0.001) and that of chemotherapy was 0.556 (P < 0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P < 0.001); the C-index was also better in the validation set (0.748 vs 0.707, P < 0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance.

Conclusions

We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia invasion and chemotherapy in predicting the survival rate of GC patients.

A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Background:

The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC).

Methods:

GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed.

Results:

A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS.

Conclusion:

The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

Personalized prediction of overall survival in patients with AML in non-complete remission undergoing allo-HCT

Allogenic hematopoietic stem cell transplantation (allo‐HCT) is the standard treatment for acute myeloid leukemia (AML) in non‐complete remission (non‐CR); however, the prognosis is inconsistent. This study aimed to develop and validate nomograms and a web application to predict the overall survival (OS) of patients with non‐CR AML undergoing allo‐HCT (cord blood transplantation [CBT], bone marrow transplantation [BMT], and peripheral blood stem cell transplantation [PBSCT]). Data from 3052 patients were analyzed to construct and validate the prognostic models. The common significant prognostic factors among patients undergoing allo‐HCT were age, performance status, percentage of peripheral blasts, cytogenetic risk, chemotherapy response, and number of transplantations. The conditioning regimen was a significant prognostic factor only in patients undergoing CBT. Compared with cyclophosphamide/total body irradiation, a conditioning regimen of ≥3 drugs, including fludarabine, with CBT exhibited the lowest hazard ratio for mortality (0.384; 95% CI, 0.266–0.554; p < 0.0001). A conditioning regimen of ≥3 drugs with CBT also showed the best leukemia‐free survival among all conditioning regimens. Based on the results of the multivariable analysis, we developed prognostic models showing adequate calibration and discrimination (the c‐indices for CBT, BMT, and PBSCT were 0.648, 0.600, and 0.658, respectively). Our prognostic models can help in assessing individual risks and designing future clinical studies. Furthermore, our study indicates the effectiveness of multi‐drug conditioning regimens in patients undergoing CBT.

Differential prognostic implications of gastric adenocarcinoma based on Lauren's classification: a Surveillance, Epidemiology, and End Results (SEER)-based cohort study

Background

Our study aims to analyze the association between Lauren's classification and gastric adenocarcinoma prognosis using comprehensive statistical analyses.

Methods

According to the selection criteria, patients were included from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression, propensity score matching, and a multivariate competing risk model were used to investigate the association between Lauren's classification and prognosis. Subgroup analysis was used to investigate the role of confounding factors on the association between Lauren types and prognosis.

Results

After exclusion, a total of 20,218 patients from the SEER database were included, with 14,374 intestinal types and 5,844 diffuse types. The univariate Cox regression analysis revealed that the diffuse type had a poorer cancer-specific survival (CSS) rate [hazard ratio (HR), 1.44; 95% confidence interval (CI), 1.38-1.50]. After adjusting for confounding variables, the diffuse type also showed a higher risk of cancer-specific death (HR, 1.20; 95% CI, 1.15-1.20). Sensitivity analysis showed that after propensity score matching, the diffuse type had a poorer CSS rate (HR, 1.23; 95% CI, 1.10-1.36), and the competing risk model further validated these results [subdistribution HR (SHR), 1.32; 95% CI, 1.23-1.41]. Moreover, subgroup analysis demonstrated stable results in the subgroups, except for patients with T1 stage (HR, 1.06; 95% CI, 0.87-1.28) and a tumor size <2 cm (HR, 1.00; 95% CI, 0.83-1.21).

Conclusions

Diffuse-type gastric adenocarcinoma had an overall poorer prognosis compared to the intestinal type. However, in patients with T1 stage and tumor size <2 cm, the diffuse type had a comparable survival rate with the intestinal type.

Predictive nomograms for oral tongue squamous cell carcinoma applying the American Joint Committee on Cancer/Union Internationale Contre le Cancer 8th edition staging system

BACKGROUND

Nomograms applying the 8th edition of the TNM staging system aimed at predicting overall (OS), disease-specific (DSS), locoregional recurrence-free (LRRFS) and distant recurrence-free survivals (DRFS) for oral tongue squamous cell carcinoma (OTSCC) are still lacking.

METHODS

A training cohort of 438 patients with OTSCC was retrospectively enrolled from a single institution. An external validation set of 287 patients was retrieved from two independent institutions.

RESULTS

Internal validation of the multivariable models for OS, DSS, DRFS and LRRFS showed a good calibration and discrimination results with optimism-corrected c-indices of 0.74, 0.75, 0.77 and 0.70, respectively. The external validation confirmed the good performance of OS, DSS and DRFS models (c-index 0.73 and 0.77, and 0.73, respectively) and a fair performance of the LRRFS model (c-index 0.58).

CONCLUSIONS

The nomograms herein presented can be implemented as useful tools for prediction of OS, DSS, DRFS and LRRFS in OTSCC.

Development and External Validation of a Nomogram to Predict Recurrence-Free Survival After R0 Resection for Stage II/III Gastric Cancer: An International Multicenter Study

Background: The benefit of adjuvant chemotherapy varies widely among patients with stage II/III gastric cancer (GC), and tools predicting outcomes for this patient subset are lacking. We aimed to develop and validate a nomogram to predict recurrence-free survival (RFS) and the benefits of adjuvant chemotherapy after radical resection in patients with stage II/III GC. Methods: Data on patients with stage II/III GC who underwent R0 resection from January 2010 to August 2014 at Fujian Medical University Union Hospital (FMUUH) (n = 1,240; training cohort) were analyzed by Cox regression to identify independent prognostic factors for RFS. A nomogram including these factors was internally and externally validated in FMUUH (n = 306) and a US cohort (n = 111), respectively. Results: The multivariable analysis identified age, differentiation, tumor size, number of examined lymph nodes, pT stage, pN stage, and adjuvant chemotherapy as associated with RFS. A nomogram including the above 7 factors was significantly more accurate in predicting RFS compared with the 8th AJCC-TNM staging system for patients in the training cohort. The risk of peritoneal metastasis was higher and survival after recurrence was significantly worse among patients calculated by the nomogram to be at high risk than those at low risk. The nomogram's predictive performance was confirmed in both the internal and external validation cohorts. Conclusion: A novel nomogram is available as a web-based tool and accurately predicts long-term RFS for GC after radical resection. The tool can also be used to determine the benefit of adjuvant chemotherapy by comparing scores with and without this intervention.

Nomogram for individually predicting overall survival in rectal neuroendocrine tumours

BACKGROUND

This study aimed to develop a nomogram that predicts the overall survival (OS) of rectal neuroendocrine tumours (NETs).

METHODS

We retrospectively analysed 310 patients with rectal neuroendocrine tumours in 5 hospitals in southern China. All of the patients were assigned to the training set. A multivariable analysis using Cox proportional hazards regression was performed using the training set, and a nomogram was constructed. It was validated on a dataset obtained from the Surveillance, Epidemiology, and End Result (SEER) database of America (n = 547).

RESULTS

In the training set, the nomogram exhibited improved discrimination power compared with the WHO grade guidelines (Herrell's C-index, 0.872 vs 0.794; p < 0.001) and was also better than the seventh AJCC TNM classification (Herrell's C-index, 0.872 vs 0.817; p < 0.001). In the SEER validation dataset, the discrimination was also excellent (C-index, 0.648 vs 0.583, p < 0.001 and 0.648 vs 0.603, p = 0.016, respectively, compared with G grade and TNM classification). Calibration of the nomogram predicted individual survival corresponding closely with the actual survival.

CONCLUSIONS

We developed a nomogram predicting 1- and 3-year OS of patients with rectal neuroendocrine tumours. Validation revealed excellent discrimination and calibration, suggesting good clinical utility.

Impact of retrieved lymph node count on short-term complications in patients with gastric cancer

BACKGROUND

It is well established that retrieved lymph node (RLN) counts were positively correlated with better overall survival in gastric cancer (GC). But little is known about the relationship between RLN count and short-term complications after radical surgery.

METHODS

A total of 1487 consecutive GC patients between January 2016 and December 2018 at Nanjing Drum Tower Hospital were retrospectively analyzed. Univariate analyses were performed to elucidate the association between RLN count and postoperative complications. We further identified clinical factors that might affect the RLN count.

RESULTS

Among all of the patients, postoperative complications occurred in 435 (29.3%) patients. The mean RLN count was 25.1, and 864 (58.1%) patients were diagnosed with lymph node metastasis. Univariate analyses showed no significant difference between RLN count and postoperative complications (both overall and stratified by CDC grade). Univariate and multivariate analyses further revealed that type of resection, tumor invasion, and lymph node metastasis were associated with RLN count.

CONCLUSIONS

The current study demonstrated that RLN count was not associated with postoperative short-term complications following gastrectomy of GC, which provided a rationale for the determination of a proper RLN count of curative gastrectomy.

Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study

PURPOSE

Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis.

METHODS

A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use.

RESULTS

In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage.

CONCLUSIONS

The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.

The clinicopathological characteristics and prognosis of patients with node-positive gastric cancer after curative surgery

BACKGROUND

Lymph node (LN) metastasis is one of the independent prognostic factors of gastric cancer (GC). The difference in survival rates and initial recurrence patterns in patients with node-positive GC with retrieved LN numbers greater than or less than 16 is worthy of further study.

METHODS

A total of 1314 patients with node-positive GC were enrolled. The clinicopathological characteristics, retrieved LN numbers, adjuvant chemotherapy, initial recurrence patterns, and survival differences between serosa-negative and serosa-positive GC were investigated.

RESULTS

For serosa-negative GC, patients with retrieved LN numbers ≥16 were associated with fewer tumor recurrences, locoregional recurrences, distant metastases, and better 5-year overall survival (OS) rates and disease-free survival (DFS) rates. For serosa-positive GC, patients with retrieved LN numbers ≥16 were associated with similar locoregional and distant metastasis and similar 5-year OS and DFS rates compared with those with retrieved LN numbers <16. Retrieved LN numbers fewer than 16 can cause stage migration compared with retrieved LN numbers ≥16. Multivariate analysis showed that both the retrieved LN numbers (≥ or <16) and adjuvant chemotherapy were independent prognostic factors affecting OS in serosa-negative GC, while adjuvant chemotherapy but not the retrieved LN numbers was an independent prognostic factor of OS in serosa-positive GC.

CONCLUSION

For serosa-negative GC, retrieved LN numbers fewer than 16 can cause stage migration, a higher tumor recurrence rate and worse OS and DFS rates compared with patients with retrieved LN numbers ≥16. Due to a high tumor recurrence rate in serosa-positive GC, adjuvant chemotherapy rather than retrieved LN numbers played an important role in improving patient prognosis.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

BACKGROUND

Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN-based survival prediction model for patients with gastric cancer.

METHODS

Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time-dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.

RESULTS

An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5-year survival prediction, respectively. In the calibration curve analysis, the ANN-predicted survival had a high consistency with the actual survival. Comparison of the DCA and time-dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.

CONCLUSIONS

The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Development and internal validation of a diagnostic score for gastric linitis plastica

BACKGROUND

There is no consensual definition for gastric linitis plastica (GLP). We aim to construct a diagnostic score to distinguish this rare tumor from usual gastric adenocarcinomas.

METHODS

In this retrospective study, all patients who had gastrectomy for cancer between 2007 and 2017 in French tertiary centers were included. The outcome was a diagnosis of GLP based on pathological review of the surgical specimen. The diagnostic score was created by using variables that were most frequently associated with GLP using penalized logistic regression on multiply imputed datasets. We used discrimination measures to assess the performances of the score. Internal validation was performed using bootstrapping methods to correct for over-optimism.

RESULTS

220 patients including 71 linitis plastica (female 49%, median age 57 years) were analyzed. The six parameters retained in the diagnosis score were the presence of large folds and/or parietal thickening on at least one segment, pangastric infiltration and presence of gastric stenosis on the upper endoscopy, circumferential thickening on at least one segment and thickening of the third hyperechogenic layer on endoscopic ultrasound and the presence of signet ring cells on endoscopic biopsies. The area under the ROC curve (AUC) was 0.967 with a sensitivity of 94% [89.9-97.3] and a specificity of 88.7% [81.7-95.8] for a threshold of 2.75. After internal validation, the corrected AUC was 0.959.

CONCLUSION

It is the first study validating a pre-therapeutic diagnostic score (Saint Louis linitis score) with an excellent ability to discriminate GLP from non-GLP adenocarcinomas. An external validation is necessary to confirm our data.

Development and validation of a nomogram for predicting overall survival of gastric cancer patients after D2R0 resection

OBJECTIVE

The aim of the study was to find factors associated with overall survival (OS) and establish a nomogram predicting OS of patients with gastric cancer (GC) after D2R0 resection.

METHODS

Demographic and clinicopathologic factors of patients with GC underwent D2R0 surgical resection were retrospectively collected from medical records, telephone interview or outpatient follow-up. To find factors significantly associated with OS, univariate and multivariate analyses were conducted. The concordance index (C-index) was used to measure the accuracy of the nomogram. Discrimination and calibration of the nomogram were tested using the patients in the validation set.

RESULTS

Overall, patients with 890 GC underwent D2R0 surgical resection were included. Based on the results of univariate analysis and multivariate analysis, T stage, number of metastatic local lymph nodes, lymph node positive rate, adjuvant chemotherapy and diameter of tumour were used to construct a nomogram predicting OS of patients with GC after surgical resection. In the validation cohort, the C-index was 0.73 and the nomogram performed well in predicting OS.

CONCLUSION

The nomogram was able to accurately predict OS of patients with GC underwent curative surgery and performed well in internal validation, which would also be useful for the decision-making of doctors.

A Nomogram Based on Preoperative Clinical Bio-Indicators to Predict 5-year Survivals for Patients with Gastric Cancer After Radical Gastrectomy

Purpose

This study aimed to improve the prediction of postoperative survival outcomes for patients with gastric cancer (GC) using a nomogram based on preoperative bio-indicators.

Patients and Methods

This retrospective study included 303 GC patients who had undergone radical gastrectomy from 2004 to 2013 at the First Affiliated Hospital, Shihezi University. The patients were followed up for 175 months after surgery and then divided into short-term (n=201) or long-term (n=102) survival groups. We used an expectation-maximization method to fill any missing data from the reviewed patient files. We then employed the Cox proportional hazard regression to identify biochemical markers that could predict 5-year overall survival (OS) as an endpoint among GC patients. Based on the results from the biochemical analysis, we developed a nomogram and assessed its performance and reliability.

Results

The variables significantly associated with OS in a multivariate analysis were age, body mass index (BMI), cell differentiation, high-density lipoprotein cholesterol (HDL-C), as well as serum potassium or serum magnesium. Combining all these predictors allowed us to establish a nomogram (C-index=0.701) whose accuracy of predicting survival was higher than the TNM staging system established by the 8th American Joint Committee on Cancer (C-index=0.666; p=0.016). Furthermore, decision curve of this nomogram was shown to have an ideal net clinical benefit rate.

Conclusion

We have developed an algorithm using preoperative bio-indicators and clinical features to predict prognosis for GC patients. This tool may help clinicians to strategize appropriate treatment options for GC patients prior to surgery.

Long-Term Survival after Minimally Invasive Versus Open Gastrectomy for Gastric Adenocarcinoma: A Propensity Score-Matched Analysis of Patients in the United States and China

BACKGROUND

This study aimed to compare the long-term survival of patients undergoing minimally invasive gastrectomy and those undergoing open gastrectomy for gastric adenocarcinoma (GA) in the United States and China.

METHODS

Data on patients with GA who underwent gastrectomy without neoadjuvant therapy were retrieved from prospectively maintained databases at Memorial Sloan Kettering Cancer Center (MSKCC) and Fujian Medical University Union Hospital (FMUUH). Using propensity score-matching (PSM), equally sized cohorts of patients with similar clinical and pathologic characteristics who underwent minimally invasive versus open gastrectomy were selected. The primary end point of the study was 5-year overall survival (OS).

RESULTS

The study identified 479 patients who underwent gastrectomy at MSKCC between 2000 and 2012 and 2935 patients who underwent gastrectomy at FMUUH between 2006 and 2014. Of the total 3432 patients, 1355 underwent minimally invasive gastrectomy, and 2059 underwent open gastrectomy. All the patients had at least 5 years of potential follow-up evaluation. Before PSM, most patient characteristics differed significantly between the patients undergoing the two types of surgery. After PSM, each cohort included 889 matched patients, and the actual 5-year OS did not differ significantly between the two cohorts, with an OS rate of 54% after minimally invasive gastrectomy and 50.4% after open gastrectomy (p = 0.205). Subgroup analysis confirmed that survival was similar between surgical cohorts among the patients for each stage of GA and for those undergoing distal versus total/proximal gastrectomy. In the multivariable analysis, surgical approach was not an independent prognostic factor.

CONCLUSIONS

After PSM of U.S. and Chinese patients with GA undergoing gastrectomy, long-term survival did not differ significantly between the patients undergoing minimally invasive gastrectomy and those undergoing open gastrectomy.

Development and external validation of a nomogram for predicting the conditional probability of survival after D2 lymphadenectomy for gastric cancer: A multicentre study

BACKGROUND

Previous studies have elucidated that on average, long-term cancer survivors have better prognoses than newly diagnosed individuals. This study aimed to devise a nomogram to predict the conditional probability of cancer-specific survival (CPCS) in gastric cancer (GC) patients after D2 lymphadenectomy.

METHODS

Clinicopathological data for 2,596 GC patients who underwent D2 lymphadenectomy in an Eastern institution (the training cohort) were retrospectively analysed. Cancer-specific survival (CSS) was predicted using Cox regression models. A nomogram was constructed to predict CPCS at 3 and 5 years post-gastrectomy. Two external validations were performed using a cohort of 2,198 Chinese patients and a cohort of 504 Italian patients.

RESULTS

In the training cohort, the 5-year CPCS was 59.2% immediately post-gastrectomy and increased to 68.8%, 79.7%, and 88.8% at 1, 2, and 3 years post-gastrectomy, respectively. Multivariate Cox regression analyses showed that age; tumour site, size and invasion depth; numbers of examined and metastatic lymph nodes; and surgical margins were independent prognostic factors of CSS (all P < 0.05) and formed the nomogram predictor variables. Internal validation showed that the conditional nomogram exhibited good discrimination ability at 3 and 5 years post-gastrectomy (concordance index, 0.794 and 0.789, respectively). External validation showed a 3- and 5-year concordance index of 0.788 and 0.785, respectively, in the Chinese cohort, and 0.792 and 0.787, respectively, in the Italian cohort. Calibration of the nomogram predicted that survival corresponded closely with actual survival.

CONCLUSIONS

we developed a robust nomogram to predict CPCS after D2 lymphadenectomy for GC based on survival duration.

Prognostic nomogram predicts overall survival in pulmonary large cell neuroendocrine carcinoma

BACKGROUND

Large cell neuroendocrine carcinoma (LCNEC) is a rare and typically aggressive malignancy with poor prognosis. This study developed a nomogram model to predict the overall survival (OS) of patients with LCNEC.

METHODS

LCNEC patients were identified from the Surveillance, Epidemiology, and End Results database between 2004-2014. Univariate and multivariate Cox regression models were used to determine demographic and clinicopathological features associated with OS. A nomogram model was generated to predict OS and its performance was assessed by Harrell's concordance index (C-index), calibration plots, and subgroup analysis by risk scores.

RESULTS

Of 3048 eligible patients with LCNEC, 2138 were randomly grouped into the training set and 910 into the validation set. Age at diagnosis, gender, tumor stage, N stage, tumor size, and surgery of primary site were independent prognostic factors of OS. C-index values of the nomogram were 0.75 (95% CI, 0.74-0.76) and 0.76 (95% CI, 0.74-0.77) in the training and validation sets, respectively. In both cohorts, the calibration plots showed good concordance between the predicted and observed OS at 3 and 5 years. Kaplan-Meier curves revealed significant differences in OS in patients stratified by nomogram-based risk score, and patients with a higher-than-median risk score had poorer OS.

CONCLUSION

This is the first nomogram developed and validated in a large population-based cohort for predicting OS in patients with LCNEC, and it shows favorable discrimination and calibration abilities. Use of this proposed nomogram has the potential to improve prediction of survival risk, and lead to individualized clinical decisions for LCNEC.

A novel prognosis prediction model after completion gastrectomy for remnant gastric cancer: Development and validation using international multicenter databases

BACKGROUND

Examined lymph node counts of remnant gastric cancer patients are often insufficient, and the prognostic ability of tumor-node-metastasis staging is therefore limited. This study aimed to create a simple and universally applicable prediction model for RGC patients after completion of gastrectomy.

METHODS

A 5-year overall survival prediction model for remnant gastric cancer patients was developed using a test dataset of 148 consecutive patients. Model coefficients were obtained based on the Cox analysis of clinicopathological factors. Prognostic performance was assessed with the concordance index (C-index) and decision curve analysis. For internal validation, the bootstrap method and calibration assessment were used. The model was validated using 2 external cohorts from China (First Affiliated Hospital of Fujian Medical University, n = 46) and the United States (Mayo Clinic, n = 20).

RESULTS

Depth of tumor invasion, number of metastatic lymph nodes, distant metastasis, and operative time were independent prognostic factors. Our model's C-index (0.761) showed better discriminatory power than that of the eighth tumor-node-metastasis staging system (0.714, P = .001). The model calibration was accurate at predicting 5-year survival. Decision curve analysis showed that the model had a greater benefit, and the results were also confirmed by bootstrap internal validation. In external validation, the C-index and decision curve analysis showed good prognostic performances in patient datasets from 2 participating institutions. Moreover, we verified the reliability of the model in an analysis of patients with different examined lymph node counts (>15 or ≤15).

CONCLUSION

Utilizing clinically practical information, we developed a universally applicable prediction model for accurately determining the 5-year overall survival of remnant gastric cancer patients after completion of gastrectomy. Our predictive model outperformed tumor-node-metastasis staging in diverse international datasets regardless of examined lymph node counts.

Nomogram-based parameters to predict overall survival in a real-world advanced cancer population undergoing palliative care

Background

Although palliative care has been accepted throughout the cancer trajectory, accurate survival prediction for advanced cancer patients is still a challenge. The aim of this study is to identify pre-palliative care predictors and develop a prognostic nomogram for overall survival (OS) in mixed advanced cancer patients.

Methods

A total of 378 consecutive advanced cancer patients were retrospectively recruited from July 2013 to October 2015 in one palliative care unit in China. Twenty-three clinical and laboratory characters were collected for analysis. Prognostic factors were identified to construct a nomogram in a training cohort (n = 247) and validated in a testing cohort (n = 131) from the setting.

Results

The median survival time was 48.0 (95% CI: 38.1–57.9) days for the training cohort and 52.0 (95% CI: 34.6–69.3) days for the validation cohort. Among pre-palliative care factors, sex, age, tumor stage, Karnofsky performance status, neutrophil count, hemoglobin, lactate dehydrogenase, albumin, uric acid, and cystatin-C were identified as independent prognostic factors for OS. Based on the 10 factors, an easily obtained nomogram predicting 90-day probability of mortality was developed. The predictive nomogram had good discrimination and calibration, with a high C-index of 0.76 (95% CI: 0.73–0.80) in the development set. The strong discriminative ability was externally conformed in the validation cohort with a C-index of 0.75.

Conclusions

A validated prognostic nomogram has been developed to quantify the risk of mortality for advanced cancer patients undergoing palliative care. This tool may be useful in optimizing therapeutic approaches and preparing for clinical courses individually.

Electronic supplementary material

The online version of this article (10.1186/s12904-019-0432-7) contains supplementary material, which is available to authorized users.

Nomograms to predict survival of stage IV tongue squamous cell carcinoma after surgery

To develop clinical nomograms for prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with stage IV tongue squamous cell carcinoma (TSCC) after surgery based on the Surveillance, Epidemiology, and End Results (SEER) program database.We collected data of resected stage IV TSCC patients from the SEER database, and divided them into the training set and validation set by 7:3 randomly. Kaplan-Meier analysis and Cox regression analysis were adopted to distinguish independent risk factors for OS and CSS. Clinical nomograms were constructed to predict the 3-year and 5-year probabilities of OS and CSS for individual patients. Calibration curves and Harrell C-indices were used for internal and external validation.A total of 1550 patients with resected stage IV TSCC were identified. No statistical differences were detected between the training and validation sets. Age, race, marital status, tumor site, AJCC T/N/M status, and radiotherapy were recognized as independent prognostic factors associated with OS as well as CSS. Then nomograms were developed based on these variables. The calibration curves displayed a good agreement between the predicted and actual values of 3-year and 5-year probabilities for OS and CSS. The C-indices predicting OS were corrected as 0.705 in the training set, and 0.664 in the validation set. As for CSS, corrected C-indices were 0.708 in the training set and 0.663 in the validation set.The established nomograms in this study exhibited good accuracy and effectiveness to predict 3-year and 5-year probabilities of OS and CSS in resected stage IV TSCC patients. They are useful tools to evaluate survival outcomes and helped choose appropriate treatment strategies.

Prognostic and Predictive Factors for the Curative Treatment of Esophageal and Gastric Cancer in Randomized Controlled Trials: A Systematic Review and Meta-Analysis

BACKGROUND

An overview of promising prognostic variables and predictive subgroups concerning the curative treatment of esophageal and gastric cancer from randomized controlled trials (RCTs) is lacking. Therefore, we conducted a systematic review and meta-analysis.

METHODS

PubMed, EMBASE, CENTRAL, and ASCO/ESMO conferences were searched up to March 2019 for RCTs on the curative treatment of esophageal or gastric cancer with data on prognostic and/or predictive factors for overall survival. Prognostic factors were deemed potentially clinically relevant according to the following criteria; (1) statistically significant (p < 0.05) in a multivariate analysis, (2) reported in at least 250 patients, and (3) p < 0.05, in ≥ 33% of the total number of patients in RCTs reporting this factor. Predictive factors were potentially clinically-relevant if (1) the p-value for interaction between subgroups was <0.20 and (2) the hazard ratio in one of the subgroups was significant (p < 0.05).

RESULTS

For gastric cancer, 39 RCTs were identified (n = 13,530 patients) and, for esophageal cancer, 33 RCTs were identified (n = 8618 patients). In total, we identified 23 potentially clinically relevant prognostic factors for gastric cancer and 16 for esophageal cancer. There were 15 potentially clinically relevant predictive factors for gastric cancer and 10 for esophageal cancer.

CONCLUSION

The identified prognostic and predictive factors can be included and analyzed in future RCTs and be of guidance for nomograms. Further validation should be performed in large patient cohorts.

Development and External Validation of Web-Based Models to Predict the Prognosis of Remnant Gastric Cancer after Surgery: A Multicenter Study

BACKGROUND

Remnant gastric cancer (RGC) is a rare malignant tumor with poor prognosis. There is no universally accepted prognostic model for RGC.

METHODS

We analyzed data for 253 RGC patients who underwent radical gastrectomy from 6 centers. The prognosis prediction performances of the AJCC7th and AJCC8th TNM staging systems and the TRM staging system for RGC patients were evaluated. Web-based prediction models based on independent prognostic factors were developed to predict the survival of the RGC patients. External validation was performed using a cohort of 49 Chinese patients.

RESULTS

The predictive abilities of the AJCC8th and TRM staging systems were no better than those of the AJCC7th staging system (c-index: AJCC7th vs. AJCC8th vs. TRM, 0.743 vs. 0.732 vs. 0.744; P>0.05). Within each staging system, the survival of the two adjacent stages was not well discriminated (P>0.05). Multivariate analysis showed that age, tumor size, T stage, and N stage were independent prognostic factors. Based on the above variables, we developed 3 web-based prediction models, which were superior to the AJCC7th staging system in their discriminatory ability (c-index), predictive homogeneity (likelihood ratio chi-square), predictive accuracy (AIC, BIC), and model stability (time-dependent ROC curves). External validation showed predictable accuracies of 0.780, 0.822, and 0.700, respectively, in predicting overall survival, disease-specific survival, and disease-free survival.

CONCLUSIONS

The AJCC TNM staging system and the TRM staging system did not enable good distinction among the RGC patients. We have developed and validated visual web-based prediction models that are superior to these staging systems.