Tumor Node Metastasis Staging System Research Articles

Blood parameters score predicts long-term outcomes in stage II-III gastric cancer patients.

BACKGROUNDIncreasing numbers of laboratory blood parameters (BPM) have been reported to greatly affect the long-term outcomes of gastric cancer (GC) patients. However, the existing prognostic models do not comprehensively analyze these predictors.AIMTo construct a new prognostic tool, based on all the prognostic BPM, to achieve more accurate prognosis prediction for GC.METHODSWe retrospectively assessed 850 consecutive patients who underwent curative resection for stage II-III GC from January 2010 to April 2013. The patients were classified into developing (n = 567) and validation (n = 283) cohorts using computer-generated random numbers. A scoring system, namely BPM score, was then constructed using least absolute shrinkage and selection operator (LASSO) Cox regression model in the developing cohort, and validated in the validation cohort. A nomogram consisting of BPM score and tumor-lymph node-metastasis (TNM) stage was further created. The discrimination and calibration of the nomogram were evaluated via Harrell’s C-statistic and the Hosmer-Lemeshow test.RESULTSUsing the LASSO model, we established the BPM score based on five BPM: Albumin, lymphocyte-to-monocyte ratio, neutrophil-to-lymphocyte ratio, carcinoembryonic antigen, and carbohydrate antigen 19-9. The BPM scores were divided into high- and low-BPM groups based on a cut-off value of -0.93. High-BPM patients were significantly older and had more advanced, larger tumors. In the developing cohort, significant differences were found in 5-year overall survival (OS) and 5-year disease-specific survival between the high-BPM and low-BPM patients. Similar results were found in the validation group. Multivariable analysis showed that the BPM score was an independent predictor of OS. High-BPM patients had a poorer 5-year OS for each subgroup. Furthermore, a nomogram that combined the BPM score and TNM stage had significantly better prognostic value compared with TNM stage alone.CONCLUSIONThe BPM score provides more accurate prognosis prediction in stage II-III GC patients and is an effective complement to the TNM staging system.

A Model for the Prediction of Survival in Patients With Upper Tract Urothelial Carcinoma After Surgery.

Objective:We aimed to formulate and validate prognostic nomograms that can be used to predict the prognosis of patients with upper tract urothelial carcinoma (UTUC).Methods:By consulting the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients who were surgically treated for UTUC between 2004 and 2013. Variables were analyzed in both univariate and multivariate analyses. Nomograms were constructed based on independent prognostic factors. The prognostic nomogram models were established and validated internally and externally to determine their ability to predict the survival of patients with UTUC.Results:A total of 4990 patients were collected and enrolled in our analyses. Of these, 3327 patients were assigned to the training set and 1663 to the validation set. Nomograms were effectively applied to predict the 3- and 5-year survivals of patients with UTUC after surgery. The nomograms exhibited better accuracy for predicting overall survival (OS) and cancer-specific survival (CSS) than the tumor-node-metastasis (TNM) staging system and the SEER stage in both the training and validation sets. Calibration curves indicated that the nomograms exhibited high correlation to actual observed results for both OS and CSS.Conclusions:The nomogram models showed stronger predictive ability than the TNM staging system and the SEER stage. Precise estimates of the prognosis of UTUC might help doctors to make better treatment decisions.

Consideration of Age Is Necessary for Increasing the Accuracy of the AJCC TNM Staging System of Pancreatic Neuroendocrine Tumors.

Purpose: Currently, of the two most common staging systems of pancreatic neuroendocrine tumors (pNETs) one is from the European Neuroendocrine Tumor Society (ENETS) and the other is from the American Joint Committee on Cancer (AJCC). However, there are imperfections in both these staging systems.Patients and methods: Patients were selected retrospectively from the Surveillance Epidemiology and End Results (SEER) database (2004 to 2013). The effect of age on the hazard ratio (HR) was evaluated using restricted cubic splines. The discriminatory power of the staging systems was determined using the concordance index (C-index).Results: A total of 3,034 patients with pNETs were included in the final analyses. The risk of death increased slowly along with age for patients under 60 years of age, but the risk of death rose sharply for those over 60 years of age, forming a mirrored L-shaped survival curve. In the current AJCC tumor-node-metastasis (TNM) staging system, no statistical significance was observed between stages IA and IB (p = 0.105). Patients with stage IIB even had longer OS than patients with IIA, although there was no statistical significance (p = 0.574). The proportion of stage III patients was small (2.7%). In the proposed aTNM staging system, significant survival differences could be observed among stage I, IIA, and IIB (p < 0.001) and the proportion of stage III rose from 2.7 to 25.7%.Conclusion: Our findings suggest that age has a critical influence on the survival of patients with pNETs. Age should be considered as a factor in future staging systems of pNETs.

Comparison of the 7th and 8th Edition of the UICC/AJCC TNM Staging System in Primary Resected Squamous Cell Carcinomas of the Lung-A Single Center Analysis of 354 Cases.

Background: The AJCC/UICC TNM (tumor, node, metastasis) classification is a standardized system for the description of anatomical extent and stage grouping of solid malignant tumors and is regularly updated. We aimed at testing the new 2017 8th edition of the TNM classification (TNM8) compared to the former 2009 7th edition (TNM7), in pulmonary squamous cell carcinomas (pSQCC).Methods: We analyzed a clinico-pathologically well-annotated Western single-center cohort of 354 consecutive pSQCC, resected 2000–2013, without previous neoadjuvant therapy. Patients with a clinical history of SQCC of other organs were excluded to reliably exclude lung metastases. Patients in whom TNM was unclear due to multiple tumor nodules were excluded. We reevaluated all pathological records and slides and retrospectively validated pleural invasion for all cases. Raw data of our cohort are provided as Supplementary Material.Results: The stage distribution according to TNM7 was as follows: IA (2009): 59 (16.7%), IB: 75 (21.2%), IIA: 71 (20.1%), IIB: 53 (15.0%), IIIA: 79 (22.3%), IIIB: 7 (2.0%), IV: 10 (2.8%). Staging the cases according to TNM8, 7/354 (2.0%) cases were down-staged, 154 (43.5%) were upstaged; most pronounced between stages IIA(TNM7) and IIB(TNM8), and IIB(TNM7) and IIIA(TNM8). Both staging systems showed significant prognostic impact for overall survival, disease free and disease specific survival and time to recurrence, without significant differences regarding goodness-of-fit criteria (Akaike Information Criterion and Schwarz Bayesian Criterion).Conclusion: In conclusion, we show a significant stage migration between tumors staged using TNM7 and TNM8, without benefit regarding prognostication in our cohort of primary resected pSQCC.

Development of a Novel Prognostic Risk Classification System for Malignant Pleural Mesothelioma

IntroductionThis study aimed to assess prognostic factors to better understand malignant pleural mesothelioma (MPM) and to develop a new classification protocol beyond the standard tumor node metastasis (TNM) staging system. Materials and MethodsWe retrospectively reviewed the data of 188 patients with MPM who had not undergone surgical resection. For each patient, we calculated the maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis (TLG) on pretreatment 18F-fluoro-2-deoxyglucose-positron emission tomography/computed tomography. Using the Cox proportional hazards model, we evaluated the relationships among potential MPM predictors, including age, gender, performance status, histologic type, stage, possible serum markers, and volume-based positron emission tomography parameters, as well as overall survival. ResultsThe median survival was 461 days, and the 1- and 2-year overall survival rates were 60.70% and 31.10%, respectively. Univariate and multivariate analyses revealed that the significant independent predictors of poor survival outcomes were the non-epithelioid histologic type, elevated serum lactate dehydrogenase levels, a neutrophil-to-lymphocyte ratio of ≥ 5.0, and a TLG of ≥ 525 g. We then used these results to develop a prognostic risk classification system. From the resulting survival curve, we found a significant difference among the 3 risk groups of independent variables. Moreover, there were significant differences between all pairs of 2 separated risk groups. ConclusionsPathologic subtypes, serum lactate dehydrogenase, neutrophil-to-lymphocyte ratio, and TLG in 18F-fluoro-2-deoxyglucose-positron emission tomography/computed tomography were independent and significant prognostic factors of MPM. Using this model, we created a new risk classification system that supplants the standard TNM staging protocol.

Assessing a modified-AJCC TNM staging system in the New South Wales Cancer Registry, Australia

BackgroundIn 2017, the New South Wales Cancer Registry (NSWCR) participated in a project, supported by Cancer Australia, aiming to provide national stage data for melanoma, prostate, colorectal, breast, and lung cancers diagnosed in 2011. Simplified business rules based on the American Joint Committee for Cancer (AJCC) Tumour-Node-Metastasis (TNM) stage were applied to obtain Registry-Derived (RD) stage, defined as the best estimate of TNM stage at diagnosis using routine notifications available within cancer registries. RD-stage was compared with Degree of Spread (DoS), which has been recorded for all applicable cancers in NSWCR at a population-based level since 1972, and a summary AJCC-TNM stage group, which has been collected variably since 2006. For each of the five high incidence cancers, we compared the level of improvements RD-staging provided in terms of completeness and accuracy (alignment to more clinically relevant AJCC-TNM) over DoS.MethodsFor each of the five cancers, stage data were extracted from NSWCR pre- and post- RD-staging to compare data completeness across all three staging systems. The alignment between DoS/RD-stage and AJCC-TNM was compared, as were the expected and observed cross-tabulated frequency distributions using a subset of NSWCR data. To determine differences between use of DoS, RD-stage, and AJCC-TNM in an epidemiological analysis, we compared survival models developed from each of the three stage variables.ResultsWe found RD-staging provided greatest stage data completeness and alignment to AJCC-TNM for prostate cancers, followed by breast, then melanoma and lung cancers. For colorectal cancer, summary stage from DoS was confirmed as an equivalent surrogate staging system to both AJCC-TNM and RD-stage.ConclusionsThis analysis provides an evidence-based approach that can be used to inform decision-making for resource planning and potential implementation of a new stage data field in population-based cancer registries.

Tumor location is an independent prognostic factor of esophageal adenocarcinoma based on the eighth edition of TNM staging system in Chinese patients.

Whether tumor location has any impact on the survival of esophageal adenocarcinoma patients remains unclear. Therefore, we aimed to investigate the prognostic value of tumor location for esophageal adenocarcinoma based on the eighth edition of tumor-node-metastasis (TNM) staging system in Chinese patients for the first time. We conducted a retrospective analysis of patients undergoing esophagectomy for esophageal adenocarcinoma in our department. We analyzed the data about demography, comorbidity, pathologic findings, surgical approach, adjuvant therapy, and survival time. Tumor location was categorized into two groups: adenocarcinomas at the esophagogastric junction (EGJ) and adenocarcinomas at other sites of the esophagus. Both univariate and multivariate analyses were applied. And propensity-score matched (PSM) analysis was also conducted for comparison. A total of 107 patients from January 2009 to December 2015 were involved in the analysis. The median follow-up time was 60.0 months and the median survival time of all those patients was 41.0 months. In the univariate analysis, adenocarcinomas in the EGJ (P=0.047), early pT stage (P=0.030), and moderate/well differentiation (P=0.022) were significantly correlated with better survival. Moreover, in the multivariate analysis, tumor site [hazard ratio (HR) =0.536; 95% confidence interval (CI) =0.300-0.958], pT stage (HR =0.298; 95% CI =0.124-0.717), and tumor differentiation (HR =0.437; 95% CI =0.238-0.802) were significant independent prognostic factors for overall survival of these esophageal adenocarcinoma patients. After the adjustment by PSM, patients with adenocarcinomas at the EGJ still yielded significantly longer survival than these with adenocarcinomas at other sites of the esophagus (P=0.039). Tumor location was an independent prognostic factor for esophageal adenocarcinoma based on the eighth edition of TNM staging system in Chinese patients. Therefore, different surgical therapeutic modalities may be applied for esophageal adenocarcinoma with different tumor locations.

Clinical and molecular prognostic markers of survival after surgery for gastric cancer: tumor-node-metastasis staging system and beyond.

For accurately predicting prognosis and for effectively describing cancer states at a certain point during treatment to other care providers and patients, various staging systems have been utilized in gastric cancer. Among these, the UICC/AJCC tumor-node-metastasis (TNM) staging system is most widely used. However, even within the same substage, gastric cancers can vary substantially in regards to prognosis after treatment. For more accurate and individualized prognostication, staging systems have been found to benefit from including molecular markers and genomic subtypes, in addition to clinicopathological parameters, such as age, sex, tumor size, tumor location, Lauren classification, number of lymph nodes resected, extent of surgical resection, lymphovascular invasion, and adjuvant chemotherapy. In this review article, we review and summarize relevant biomarkers for gastric cancer that can be incorporated into the current anatomy-based TNM staging system, as well as results from validation studies thereof.

CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk

To estimate recurrence risk after surgery in nonsmall cell lung cancer (NSCLC) patients by employing tumoral and peritumoral radiomics analysis. One-hundred twenty-four surgically treated stage IA-IIB NSCLC patients' data from 2008 to 2013 were retrospectively collected. Patient outcome was defined as local recurrence (LR), distant metastasis (DM), and (sum of LR and DM) total recurrence (TR) at follow-up. Volumetric region of interests (ROIs) were drawn for the tumor, peritumoral lung parenchyma (2 cm around the tumor) and involved lobe on CT images. Ninety-four (morphological, first-order, textural, fractal-based) radiomics features were extracted from the ROIs and datasets were created from single or combined ROIs. Predictive models were built with radiomics signature (RS) and clinicopathological data, and the area under the curve (AUC) was used to evaluate the performance. Radiomics score was calculated with the best models' feature coefficients, low- and high-risk groups of patients defined accordingly. Kaplan-Meier curves were built, and the log-rank test was used for comparison among low- and high-risk groups. Differences in recurrence risk among the two risk groups were calculated (chi-square test). Fifty-six patients developed TR (25 LR, 31 DM). The tumor-node-metastasis (TNM) stage recurrence predictability (AUCTR 0.680; AUCDM 0.672; AUCLR 0.580) was substantially improved when RS was added to the predictive model (AUCTR 0.760; AUCDM 0.759; AUCLR 0.750). Seventy-five percent of high-risk patients developed TR. Recurrence risk of the high-risk group was 16-fold higher than that of the low-risk group (p < 0.001). Combination of the tumoral and peritumoral RS with TNM staging system outperformed TNM staging alone in individualized recurrence risk estimation of patients with surgically treated NSCLC.

The new 8th TNM staging system of lung cancer and its potential imaging interpretation pitfalls and limitations with CT image demonstrations.

The tumor, node, metastasis (TNM) staging system approved by International Association for the Study of Lung Cancer (IASLC) and the American Joint Committee on Cancer (AJCC) to stage lung cancer was recently revised. The latest revision is the 8th edition published in January, 2017. This new edition made some important changes to the previous edition, including modification of the T classification based on 1 cm increment, downstage of T descriptor including endobronchial tumor disregarding its distance from carina (T2), merging total and partial atelectasis/pneumonitis into the same T category (T2), upstage diaphragmatic invasion to T4, new classification concept of adenocarcinoma in situ and minimally invasive adenocarcinoma for pure and part-solid ground-glass nodules, and further division of extrathoracic metastasis into M1b and M1c based on the number and sites of extrathoracic metastases. Consensus is reached for debating situations not covered in the previous edition of staging system, such as the classification of pancoast tumor based on its invasion depth and staging tumors that extend directly across the fissure as T2a. Classification of multiple sites of pulmonary involvement, including multiple primary lung cancer, separate lung cancer nodules, multiple ground-glass or lepidic lesions, and consolidation, is also discussed. Even though the 8th edition of the TNM lung staging system provides us with more precise classification based on prognostic analysis of each TNM descriptors, there are still some potential limitations and clinical situations that have not yet been clarified in terms of clinical staging by imaging. It is important for radiologists to understand the major changes introduced in the 8th edition of TNM staging and to recognize the potential pitfalls and limitations of imaging interpretation to precisely classify the clinical stage of lung cancer.

Visceral pleural invasion in T1 tumors (≤3 cm), particularly T1a, in the eighth tumor-node-metastasis classification system for non-small cell lung cancer: a population-based study.

We aimed to validate the tumor (T) descriptors of visceral pleural invasion (VPI) for T1 tumors (<3 cm) in the 8th edition of the tumor-node-metastasis (TNM) classification system and the prognostic value of VPI for resected T1a tumors. The external cohort consisted of 23,501 patients with resected pN0 non-small cell lung cancer (NSCLC) selected from the Surveillance, Epidemiology, and End Results (SEER) database (2010 to 2013). The classification of T1 tumors with VPI was investigated using survival curves. The internal cohort consisted of patients diagnosed with pN0 NSCLC between 2011 and 2013 at Guangdong Lung Cancer Institute. The prognostic value of VPI for T1a tumors (<1 cm) was further assessed in these two cohorts. The overall survival (OS) and lung cancer-specific survival (LCSS) of the T1-VPI group and groups of each T stage (size only) were compared in the external (SEER) cohort. There were no significant survival differences between the T1-VPI and T2a groups (OS: P=0.706; LCSS: P=0.792) and T1-VPI and T2b groups, although the latter showed a trend toward lower P-values (OS: P=0.117; LCSS: P=0.094). In the internal cohort, a significant difference in OS was observed between patients with T1-VPI and those with T2b (P=0.049). Among patients with T1a tumors and VPI in the SEER database, the prognosis of the non-sub-lobectomy group was superior to that of the sub-lobectomy group, with intrathoracic recurrence as the predominant relapse pattern of T1 tumors with VPI (69.2%). T1 tumors (<3 cm) with VPI can be staged as T2a in the 8th TNM staging system and surgical resection of T1a tumors is a concern when VPI is present.

Overexpression of PODXL/ITGB1 and BCL7B/ITGB1 accurately predicts unfavorable prognosis compared to the TNM staging system in postoperative pancreatic cancer patients.

We previously reported that overexpression of PODXL, BCL7B, and ARHGEF4 in pancreatic cancer tissue is correlated with pancreatic cancer-related survival. The aim of this study was to investigate the use of PODXL, BCL7B, ARHGEF4, and the integrin family member ITGB1 as useful markers for the prognosis of postoperative pancreatic cancer patients in comparison with tumor size and the tumor node metastasis (TNM) staging system. Immunohistochemistry was performed using an anti-ITGB1 antibody on 102 samples of pancreatic cancer tissue surgically resected at the University of Kochi Medical School Hospital and the Matsuyama Shimin Hospital. Univariate Cox proportional hazards regression analysis showed that TNM stage and overexpression of PODXL, BCL7B, and ITGB1 were correlated with postoperative survival. However, tumor size was not significantly associated with postoperative prognosis of pancreatic cancer compared to these features. Multivariate Cox proportional hazards regression analysis showed that the overexpression of both PODXL and ITGB1 and overexpression of both BCL7B and ITGB1 increased the hazard ratio (6.27, 95% confidence interval [CI] 2.58–15.21; and 3.93, 95% CI 1.74–8.91, respectively) compared to that of TNM stage (IIA and IIB vs. III and IV; 3.05, 95% CI 1.25–7.42). These results imply that the combination of PODXL with ITGB1 and the combination of BCL7B with ITGB1 accurately predicted the postoperative outcomes of pancreatic cancer patients, and they were superior compared to the TNM staging system. The combination of PODXL with ITGB1 would be particularly useful, as it was the most highly correlated with postoperative outcomes. Importantly, the present results are useful to determine which adjuvant therapy should be selected.

The prognostic value of the preoperative albumin to alkaline phosphatase ratio in patients with non-small cell lung cancer after surgery.

BackgroundTo assess the potential prognostic value of the albumin to alkaline phosphatase ratio (AAPR) in patients with non‐small cell lung cancer (NSCLC) after surgery.MethodsThe log‐rank and Kaplan‐Meier analyses were performed to detect differences in survival levels between different groups. A model of Cox proportional hazards was used to perform univariate and multivariate survival analyses. Comparisons of receiver operating characteristic (ROC) curves and the likelihood ratio test (LRT) were also utilized to compare the prognostic abilities of different systems for overall survival (OS) prediction.ResultsThe optimal cut‐off value of the preoperative AAPR was 0.64. A decreased AAPR was associated with several clinicopathological and clinicolaboratory variables related to cancer progression. The preoperative AAPR of patients was positively correlated with the poor prognosis of NSCLC. In multivariate analyses, the preoperative AAPR was identified as an independent prognostic factor for disease‐free survival (DFS; P = 0.001) and overall survival (OS; P = 0.003). The LRT showed that the AAPR tumor‐node‐metastasis (TNM) system presented a significantly larger χ2 value (112.4 vs. 89.2, respectively, P < 0.01) and a relatively smaller Akaike information criterion (AIC) value (2955 vs. 2977, respectively, P < 0.01) than the TNM staging system.ConclusionPreoperative AAPR was a potentially valuable prognostic factor in NSCLC patients who underwent surgery. Our results further showed that the AAPR‐TNM system was superior to the current TNM staging system.

MiR-222 expression is correlated with the ATA risk stratifications in papillary thyroid carcinomas.

Background:miR-222 is one of the most consistently overexpressed miRNAs in papillary thyroid carcinoma (PTC). Previous studies demonstrated that miR-222 overexpression conferred high-risk features in PTC patients, suggesting its value in risk-stratification. However, studies in term of miR-222's utility on stratifying PTCs are lacking.Methods:One hundred patients including 10 with multinodular goiter and 90 with PTC were enrolled. Formalin-fixed paraffin-embedded samples were exploited for miR-222 quantitative reverse transcriptase- polymerase chain reaction (RT-PCR) analysis. Correlations between miR-222 expression and different clinicopathological features, Tumor-node-metastasis (TNM) staging and ATA risk level were analyzed.Results:miR-222 expression of the PTC group was significantly higher than that of the goiter group (P < .001). Furthermore, miR-222 expression was significantly higher in PTCs with advanced features like larger tumor, capsular invasion, vascular invasion and lymph nodes metastasis. The majority of patients (61%) were in stage I group (similar to ATA low-risk) by TNM staging system. As to the ATA system, the majority (73%) were in intermediate-risk group (similar to TNM stage II and III roughly). Contrary to previous report, here we found that miR-222 expression was correlated with the ATA risk level (P < .001), but not with the TNM staging (P = .122).Conclusion:In the present study, we demonstrated that miR-222 overexpression was correlated with advanced features like capsular invasion, vascular invasion, larger tumor size and lymph node metastasis in PTCs. Most importantly, miR-222 expression was correlated with ATA risk levels, suggesting its potential value in PTC risk-stratification.

Application of the proposed eighth edition of the American Joint Committee on Cancer/Union of International Cancer Control esophageal cancer staging system in esophageal cancer patients

AbstractObjectiveThe eighth edition of the American Joint Committee on Cancer/Union of International Cancer Control esophageal cancer staging system was proposed in early 2018. The eighth edition staging system includes clinical staging, pathological staging, and neoadjuvant pathological staging, which fills in the blank of non‐operative staging of esophageal cancer. However, there are few reports on the eighth edition of clinical staging, and the purpose of the present study was to verify the prognostic value of the new American Joint Committee on Cancer clinical staging system for esophageal cancer patients who undergo radical radiation therapy.MethodsA total of 544 patients with esophageal cancer from Tianjin Medical University Cancer Institute and Hospital between March 2010 and September 2016 were staged according to the eighth American Joint Committee on Cancer clinical staging system. The Kaplan–Meier method was used to calculate the survival rate of the patients. The Cox regression model was used for multivariate prognostic analysis.ResultsAll the patients were divided into different groups by the eighth American Joint Committee on Cancer clinical tumor–node–metastasis staging system: 40 patients with T2; 157 patients with T3; 347 patients with T4; 132 patients with N0; 193 patients with N1; 172 patients with N2; 47 patients with N3; 81 patients with stage II; 102 patients with state III; and 361 patients with stage IVA. The 3‐year survival rates of stage T2, stage T3, and stage T4 were 60.1%, 45.6%, and 30.8%, respectively (P &lt; 0.001). The 3‐year survival rates of stage N0, stage N1, stage N2, and stage N3 were 48.2%, 40.7%, 29.4%, and 17.3%, respectively (P &lt; 0.001). The 3‐year survival rates of stage II, stage III, and stage IVA were 59.3%, 40.1%, and 31.2%, respectively (P &lt; 0.001). In univariate analysis, age, concurrent chemoradiotherapy, T stage, N stage, Karnofsky marking system, the sixth edition staging system and the eighth edition staging system were important prognostic factors in univariate analyses. In multivariate analysis, concurrent chemotherapy and N stage were important prognostic factors.ConclusionsBased on the data from Tianjin Medical University Cancer Institute and Hospital, the eighth edition clinical staging system had a better predictive ability for esophageal cancer patients who underwent radical radiation therapy.

Modified American Joint Committee on Cancer Tumor-Node-Metastasis Staging System Based on the Node Ratio Can Further Improve the Capacity of Prognosis Assessment for Gastric Cancer Patients.

Background and Objectives: Our aim was to investigate whether the modified American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system based on the node ratio can further improve the capacity of prognosis assessment for gastric cancer (GC) patients regardless of the number of lymph nodes examined (eLNs).Methods: A total of 17,187 GC patients in the Surveillance, Epidemiology, and End Results (SEER) database were included. On the basis of a training set of 7,660 GC patients, we built the tumor-node ratio-metastasis (TNrM) staging system, which was then externally validated with a validation set of 9,527 GC patients.Results: For the training set, the C-index value of the TNrM staging system was significantly higher than that of the AJCC 8th TNM staging system to predict survival for GC patients (C-index: 0.688 vs. 0.671, P < 0.001). Moreover, the C-index value of the TNrM staging system was significantly higher than that of the 8th TNM staging system to predict survival for GC patients with ≤15 eLNs (C-index: 0.682 vs. 0.673, P < 0.001), as well as for GC patients with >15 eLNs (C-index: 0.700 vs. 0.694, P < 0.001). Similar results were found in the validation set.Conclusions: The TNrM staging system predicted survival more accurately and discriminatively than the AJCC 8th TNM staging system for GC patients regardless of the number of eLNs.

Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma

Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual patients. To develop a prediction model using machine learning for 5-year overall survival among patients with oral squamous cell carcinoma and compare this model with a prediction model created from the TNM (Tumor, Node, Metastasis) clinical and pathologic stage. A retrospective cohort study was conducted of 33 065 patients with oral squamous cell carcinoma from the National Cancer Data Base between January 1, 2004, and December 31, 2011. Patients were excluded if the treatment was considered palliative, staging demonstrated T0 or Tis, or survival or staging data were missing. Patient, tumor, treatment, and outcome information were obtained from the National Cancer Data Base. The data were split into a distribution of 80% for training and 20% for testing. The model was created using 2-class decision forest architecture. Permutation feature importance scores were used to determine the variables that were used in the model's prediction and their order of significance. Statistical analysis was conducted from August 1, 2018, to January 10, 2019. Ability to predict 5-year overall survival assessed through area under the curve, accuracy, precision, and recall. Among the 33 065 patients in the study, the mean (SD) age was 64.6 (14.0) years, 19 791 were men (59.9%), 13 274 were women (40.1%), and 29 783 (90.1%) were white. At 60 months, there were 16 745 deaths (50.6%). The median time of follow-up was 56.8 months (range, 0-155.6 months). Age, pathologic T stage, positive margins at the time of surgery, lymph node size, and institutional identification were identified among the most significant variables. The calculated area under the curve for this machine learning model was 0.80 (95% CI, 0.79-0.81), accuracy was 71%, precision was 71%, and recall was 68%. In comparison, the calculated area under the curve of the TNM staging system was 0.68 (95% CI, 0.67-0.70), accuracy was 65%, precision was 69%, and recall was 52%. Using machine learning algorithms, a prediction model was created based on patient social, demographic, clinical, and pathologic features. The developed prediction model proved to be better than a prediction model that exclusively used TNM pathologic and clinical stage according to all performance metrics. This study highlights the role that machine learning may play in individual patient risk estimation in the era of big data.

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma.

BackgroundLung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classification, increasing evidence has provided insights into genomic mutations and rearrangements among individual histologic subtypes of LUAD. However, how genotypic and phenotypic features of LUAD are interconnected is not well understood.MethodsWe obtained the genomic, transcriptomic, and clinical data sets of 488 LUAD patients from The Cancer Genome Atlas database. Advanced statistical models were used to disentangle the interactions between genetic mutations and expression profiles, and to assess the alterations and changes in expression of each histologic subtype. The prognostic impacts of genetic mutations, expression profiles, and clinicopathological features were integrated to predict the outcomes of LUAD patients.ResultsFrom our data, one or more genetic mutations correlate with expression levels of 6054/18175 (33.3%) genes and explain 8–40% of observed variability in LUAD. The genetic mutations and expression profiles varied remarkably among the histologic subtypes of LUAD, which helped to explain the different prognostic impact based on subtype classification. Genomic, transcriptomic, and clinical data were all shown to have utility for predicting overall and recurrence‐free survival, with the largest contribution from the transcriptome.ConclusionOur prediction model integrating genetic mutations, expression profiles, and clinicopathological features exhibited superior accuracy over the current tumor node metastasis staging system to prognosticate outcomes of patients with LUAD (overall survival 67% vs. 55%, recurrence‐free survival 57% vs. 49%; P < 0.01).

Effect of histological subtype and treatment modalities on T1-2 N0-1 small cell lung cancer: A population-based study.

BackgroundCombined small cell lung cancer (C‐SCLC) is rare and its clinical features, appropriate treatment, and prognosis are poorly understood. Reports conflict over the prognosis of C‐SCLCs compared to pure small cell lung cancer.MethodsThe records of patients diagnosed with primary SCLC from 1988 to 2014 were extracted from the Surveillance, Epidemiology, and End Results database. The general features of C‐SCLCs were compared to those of SCLCs. T1–2 N0–1 data was extracted and the effects of the histological subtype, treatment modality, and other prognostic factors on lung cancer‐specific survival (CSS) was analyzed in a 3:1 matched dataset. Analysis was performed using the 8th edition tumor node metastasis staging system and previous staging systems adjunctively.ResultsC‐SCLCs comprised 1.5% of all SCLCs (1486/98 667); 184 cases of C‐SCLCs and 2681 cases of non‐combined SCLCs (NC‐SCLCs) were included in this study. C‐SCLCs were more likely to be of a higher grade and to occur in the upper lobe than NC‐SCLCs. Before matching, C‐SCLCs showed better CSS (hazard ratio 0.69; P < 0.001). However, stratified Cox proportional hazards analysis in the matched dataset revealed that only treatment modality and age at diagnosis were associated with CSS; the histological subtype had no effect on survival. Of all treatment modalities, surgery with chemoradiation showed the best CSS in T1–2 N0–1 SCLC.ConclusionIn early SCLC, surgery with chemoradiation shows the best CSS. C‐SCLC patients might benefit more from multimodal treatments, including surgery, than SCLC patients.

A Preliminary Study of Serum Apelin Levels in Patients with Head and Neck Cancer.

Treatment planning is primarily based on the tumor node metastasis (TNM) staging system for head and neck cancer (HNC). However, TNM does not give sufficient information about biological aggressiveness, treatment response, and prognosis. New molecular markers are needed for individualized cancer treatment. Apelin is a bioactive peptide and an endogenous ligand for the G protein-coupled receptor (APJ). Its expression is induced under hypoxic conditions. Apelin and its receptor APJ are important factors in physiological angiogenesis and may be novel targets for anti-angiogenic tumor therapies. This preliminary study aimed to investigate whether there was a difference in serum apelin levels between patients with HNC and control group and also to compare the serum apelin levels before and after radiotherapy. Twenty-two patients with HNC (patient group) and 30 healthy individuals (control group) were included in the study. In the patient group, blood samples were collected before and after radiotherapy. Serum apelin-36 levels were measured by enzyme-linked immunosorbent assay. Serum apelin-36 levels were significantly higher in patients with HNC than in the control group (p<0.001). Coverage of the measured apelin-36 levels showed a significant decrease after radiotherapy according to the levels before radiotherapy. There was no statistically significant difference between the groups (p>0.05). Apelin may be a potential therapeutic target and a novel biomarker. Additional studies are needed to reveal the relationships between serum apelin and radiotherapy in solid human tumors.