Tumor Node Metastasis Staging System Research Articles

Evaluation of the prognostic value of the new 9th edition Tumor-Node-Metastases (TNM) staging system for epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma patients with bone metastases

BackgroundThere are some changes in the new 9th edition Tumor-Node-Metastases (TNM) staging system for lung cancer, including subdividing M1c into M1c1 and M1c2 stage. The aim of this study was to assess the prognostic performance of the updated classification system and try to provide some real-world application data among advanced lung adenocarcinoma patients with bone metastases.MethodsAdvanced lung adenocarcinoma patients in M1c stage with bone metastases who receiving first-line first-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and T790M-guided osimertinib as the second-line therapy were retrospectively screened from December 2016 to December 2021. A total of 126 patients were enrolled in this study. 62 patients and 64 patients were subdivided into M1c1 and M1c2 groups according to the 9th edition of TNM staging system.The first-line real-world progression-free survival (1LrwPFS), the second-line real-world progression-free survival (2LrwPFS), post-progression survival (PPS) and real-world overall survival (rwOS) were analyzed.ResultsThe overall median rwOS was 40.1 months (95% CI 35.996–44.204). 1LrwPFS was 13.9 months (95% CI 12.653–15.147) and 2LrwPFS was 14.5 months (95% CI 11.665–17.335) for all patients.Patients in M1c2 stage was inferior to M1c1 stage patients in rwOS (35.2 months vs. 42.9 months, HR = 0.512, P = 0.005). 2LrwPFS was moderately correlated with rwOS (r = 0.621, R2 = 0.568, P = 0.000). Multivariate analysis showed performance status (PS) score ≥ 2 and TP53 alteration positive were independent prognostic factors of worse rwOS.ConclusionsMore refined stratification of M1c according to the 9th edition of TNM staging system is conducive to the judgment of prognosis and the implementation of precision medicine for patients.

Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.

As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the numberoflymphnodemetastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC. This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning. A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model). The overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients. The hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Validation of the cutoff values for the number of metastatic lymph nodes for esophageal cancer staging: a multi-institutional analysis of 655 patients in Japan.

The number of metastatic lymph nodes (LNs) is an important prognostic factor for esophageal cancer, and N staging is important for prognostic stratification. The optimal cutoff values for clinical (cN) and pathologic N (pN) staging should be reconsidered following advances in neoadjuvant therapy. The study included 655 patients who underwent esophagectomy between January 2014 and December 2016 in four high-volume centers in Japan. Optimal cutoff values for the number of metastatic LNs in cN and pN staging were examined using X-tile, and their prognostic performance was validated using the Kaplan-Meier method. The cutoff values were 1, 2, and 3 for cN staging and 1, 3, and 7 for pN staging. Prognosis was significantly better in patients with cN0 than in those with modified (m)-cN1 (p = 0.0211). However, prognosis was not significantly different among the patients with m-cN1, m-cN2, and m-cN3 disease. Prognosis was significantly different among the patients with pN0, pN1, pN2, and pN3 disease (pN0 vs pN1, p < 0.0001; pN1 vs pN2, p < 0.0001; pN2 vs pN3, p < 0.0001). In patients who received preoperative neoadjuvant therapy, prognosis, which was not significantly different among the patients with cN0, m-cN1, m-cN2, and m-cN3 disease (cN0 vs m-cN1, p = 0.5675; m-cN1 vs m-cN2, p = 0.4425; m-cN2 vs m-cN3, p = 0.7111), was significantly different among the patients with pN0, pN1, pN2, and pN3 disease (pN0 vs pN1, p = 0.0025; pN1 vs pN2, p = 0.0046; pN2 vs pN3, p = 0.0104). cN has no prognostic impact in patients who underwent preoperative treatment followed by esophagectomy, despite the optimization of cN classification. The conventional TNM8th pN classification is useful for predicting prognosis even for patients who have undergone preoperative treatment. The conventional cutoffs for metastatic LNs in the International Union against Cancer tumor node metastasis staging system are valid and can be effectively used in clinical practice.

Prognostic Role of Tumor-Infiltrating Lymphocytes in Oral Squamous Cell Carcinoma

BackgroundIn oral squamous cell carcinoma (OSCC), the tumor-node-metastasis (TNM) staging system is a significant factor that influences prognosis and treatment decisions for OSCC patients. Unfortunately, TNM staging does not consistently predict patient prognosis and patients with identical clinicopathological characteristics may have vastly different survival outcomes. Host immunity plays an important role in tumor progression but is not included in the TNM staging system. Tumor-infiltrating lymphocytes (TILs) are part of the host immune response that recognizes tumor cells; and the presence of TILs has emerged as potential candidates for prognostic markers for many types of cancers. The present study aims to determine the association of T cell-specific markers (CD3, CD4, CD8, and FOXP3) with clinicopathological characteristics and survival outcomes in OSCC patients. The prognostic value of CD3, CD4, and CD8 will also be evaluated based on tumor stage.MethodsTissue microarrays were constructed containing 231 OSCC cases and analyzed by immunohistochemical staining for the expression of CD3, CD4, CD8, and FOXP3. The expression scores for each marker were correlated with clinicopathological parameters and survival outcomes. The prognostic impact of CD3, CD4 and CD8 were further analyzed based on tumor stage (early or advanced).ResultsCD3, CD4, and CD8 were found to be significantly associated with both overall survival and progression-free survival using univariate analysis. However, none of these markers were found to independently predict the survival outcomes of OSCC using multivariate analysis. Only conventional factors such as nodal status, tumor differentiation and perineural invasion (PNI) were independent predictors of survival outcomes, with nodal status being the strongest independent predictor. Additionally, low CD4 (but not CD3 or CD8) expression was found to identify early-stage OSCC patients with exceptionally poor prognosis which was similar to that of advanced staged OSCC patients.ConclusionsTIL markers such as CD3, CD4, CD8, and FOXP3 can predict the survival outcomes of OSCC patients, but do not serve as independent prognostic markers as found with conventional factors (i.e. nodal status, tumor differentiation and PNI). CD4 expression may assist with risk stratification in early-stage OSCC patients which may influence treatment planning and decision making for early-stage OSCC patients.

Inflammatory Burden Index as a promising new marker for predicting surgical and oncological outcomes in colorectal cancer.

The prognosis of colorectal cancer (CRC) has been historically reliant on the Tumor Node Metastasis (TNM) staging system, but there is variability in outcomes among patients at similar stages. Therefore, there is an urgent need for more robust biomarkers. The aim of this study was to assess the clinical feasibility of the recently reported Inflammatory Burden Index (IBI) for predicting short- and long-term outcomes in patients with CRC. This was a retrospective observational study of 555 CRC patients undergoing surgery for primary tumor resection. We determined the prognostic value of preoperative IBI for disease-free and overall survival, and its predictive value for perioperative risk of infectious complications, including surgical site infection. Increased preoperative IBI was significantly associated with advanced disease stage and poor oncological outcome in CRC patients. Higher IBI was independently linked to poorer disease-free and overall survival. Similar outcomes were observed in a subanalysis focused on high-risk stage II and stage III CRC patients. Elevated preoperative IBI was significantly correlated with an increased risk of surgical site infection and other postoperative infectious complications. Propensity score-matching analysis validated the impact of IBI on the prognosis in CRC patients. We established preoperative IBI as a valuable predictive biomarker for perioperative risks and oncological outcomes in CRC patients. Preoperative IBI is useful for designing effective perioperative management and postoperative oncological follow-up.

Predictive potential of preoperative Naples prognostic score-based nomogram model for the prognosis in surgical resected thoracic esophageal squamous cell carcinoma patients: A retrospective cohort study.

The present study aimed to establish an effective prognostic nomogram model based on the Naples prognostic score (NPS) for resectable thoracic esophageal squamous cell carcinoma (ESCC). A total of 277 patients with ESCC, who underwent standard curative esophagectomy and designated as study cohort, were retrospectively analyzed. The patients were divided into different groups, including NPS 0, NPS 1, NPS 2, and NPS 3 or 4 groups, for further analysis, and the results were validated in an external cohort of 122 ESCC patients, who underwent surgery at another cancer center. In our multivariate analysis of the study cohort showed that the tumor-node-metastasis (TNM) stage, systemic inflammation score, and NPS were the independent prognostic factors for the overall survival (OS) and progression-free survival (PFS) durations. In addition, the differential grade was also an independent prognostic factor for the OS in the patients with ESCC after surgery (all P < .05). The area under the curve of receiver operator characteristics for the PFS and OS prediction with systemic inflammation score and NPS were 0.735 (95% confidence interval [CI] 0.676-0.795, P < .001) and 0.835 (95% CI 0.786-0.884, P < .001), and 0.734 (95% CI 0.675-0.793, P < .001) and 0.851 (95% CI 0.805-0.896, P < .001), respectively. The above independent predictors for OS or PFS were all selected in the nomogram model. The concordance indices (C-indices) of the nomogram models for predicting OS and PFS were 0.718 (95% CI 0.681-0.755) and 0.669 (95% CI 0.633-0.705), respectively, which were higher than that of the 7th edition of American Joint Committee on Cancer TNM staging system [C-index 0.598 (95% CI 0.558-0.638) for OS and 0.586 (95% CI 0.546-0.626) for PFS]. The calibration curves for predicting the 5-year OS or PFS showed a good agreement between the prediction by nomogram and actual observation. In the external validation cohort, the nomogram discrimination for OS was better than that of the 7th edition of TNM staging systems [C-index: 0.697 (95% CI 0.639-0.755) vs 0.644 (95% CI 0.589-0.699)]. The calibration curves showed good consistency in predicting the 5-year survival between the actual observation and nomogram predictions. The decision curve also showed a higher potential of the clinical application of predicting the 5-years OS of the proposed nomogram model as compared to that of the 7th edition of TNM staging systems. The preoperative NPS-based nomogram model had a certain potential role for predicting the prognosis of ESCC patients.

Implications of Pretreatment Serum Carcinoembryonic Antigen Levels and Perineural Invasion with Staging, Prognosis, and Management in Stage I-III Colon Cancer after Surgery: A Retrospective Cohort Study in the SEER Database.

Pretreatment levels of serum carcinoembryonic antigen (CEA) and perineural invasion (PNI) are related to poor prognosis in colon cancer. We analyzed the CEA and PNI (defined as incorporation of carcinoembryonic antigen and perineural invasion (CP)-stage), which are included in the Tumor-Node-Metastasis (TNM) staging system of the American Joint Committee on Cancer (AJCC), and evaluated the survival prognosis of patients treated with surgery in I-III stage colon carcinoma. We employed a retrospective study for eligible colon carcinoma patients obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Kaplan-Meier curve and Multivariate Cox regression analysis were used to analyze different TNM-CP stages for the cancer-specific survival (CSS) probabilities in colon cancer. In our study, CEA levels and PNI were significant prognostic factors (p < 0.05), and the newly proposed CP-stage was an independent prognostic index in stage I-III colon carcinoma after surgery. Multivariate Cox regression analyses indicated that CP1-stage was related to a 63.9% increased risk of cancer-specific mortality (hazard ratio (HR): 1.639, 95% confidence interval (CI): 1.544-1.739, p < 0.001), compared with CP0-stage in colon cancer. In respective TNM stages, the CP0-stage had an advantage over the CP1-stage for CSS (p < 0.001). Moreover, CP1-stage patients with node-negative colon cancer were contacted with similar or worse survival in comparison to CP0-stage patients with node-positive. For postoperative patients with stage I-III colon cancer, our study indicated that the CP stage is a significant prognostic factor for CSS, which deserves more clinical attention. It's worth noting that including the CP stage in the AJCC TNM staging system of colon carcinoma is beneficial to the survival prediction and clinical treatment.

Circular RNA COL1A1 promotes Warburg effect and tumor growth in nasopharyngeal carcinoma

ObjectiveCircular RNAs (circRNAs), pivotal in the pathogenesis and progression of nasopharyngeal carcinoma (NPC), remain a significant point of investigation for potential therapeutic interventions. Our research was driven by the objective to decipher the roles and underlying mechanisms of hsa_circ_0044569 (circCOL1A1) in governing the malignant phenotypes and the Warburg effect in NPC.MethodsWe systematically collected samples from NPC tissues and normal nasopharyngeal epithelial counterparts. The expression levels of circCOL1A1, microRNA-370-5p (miR-370-5p), and prothymosin alpha (PTMA) were quantitatively determined using quantitative polymerase chain reaction (qPCR) and Western blotting. Transfections in NPC cell lines were conducted using small interfering RNAs (siRNAs) or vectors carrying the pcDNA 3.1 construct for overexpression studies. We interrogated the circCOL1A1/miR-370-5p/PTMA axis's role in cellular functions through a series of assays: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide for cell viability, colony formation for growth, Transwell assays for migration and invasion, and Western blotting for protein expression profiling. To elucidate the molecular interactions, we employed luciferase reporter assays and RNA immunoprecipitation techniques.ResultsOur investigations revealed that circCOL1A1 was a stable circRNA, highly expressed in both NPC tissues and derived cell lines. A correlation analysis with clinical pathological features demonstrated a significant association between circCOL1A1 expression, lymph node metastasis, and the tumor node metastasis staging system of NPC. Functionally, silencing circCOL1A1 led to substantial suppression of cell proliferation, migration, invasion, and metabolic alterations characteristic of the Warburg effect in NPC cells. At the molecular level, circCOL1A1 appeared to modulate PTMA expression by acting as a competitive endogenous RNA or 'sponge' for miR-370-5p, which in turn promoted the malignant characteristics of NPC cells.ConclusionTo conclude, our findings delineate that circCOL1A1 exerts its oncogenic influence in NPC through the modulation of the miR-370-5p/PTMA signaling axis.

Abstract 5065: Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer

Abstract Introduction:Stage is the most important prognostic factor for survival in non-small cell lung cancer (NSCLC). The current TNM (Tumor, Node, Metastasis) staging system for NSCLC uses physical exam, histology, imaging, and surgical findings to define the extent and spread of a patient’s cancer. Despite advances in molecular testing which can impact treatment and prognosis for NSCLC patients, the current system does not incorporate molecular data into staging. We proposed a novel predictive model for survival analysis which integrates molecular analysis of liquid biopsies (B) into the TNM staging system (1). In this study, we tested this new staging system and our predictive model shows TNM + B (TNMB) provides a better predictive model for survival compared to TNM alone. Methods:176 patients were identified at Atrium Health Wake Forest Baptist with Guardant 360 liquid biopsies sent at diagnosis for NSCLC. Clinical data was obtained through retrospective chart review. Molecular analysis was performed on 81 genes across all Guardant 360 liquid biopsies. Using Cox proportional hazards model, we identified significant gene mutations or genes with significant magnitude of variant allele frequency (VAF) that improved survival prediction. We then designed a novel staging system incorporating whether a patient had a positive liquid biopsy test (B1), defined by the presence of these impact gene variables, to TNM stage. Results: Three impact genes STK11 (p=0.0253), NFE2L2 (p = 0.0025), TP53 (p=0.0129) and one gene with a magnitude of VAF, ARID1A (p=0.0082) was identified which significantly improved the predictive model for survival. Patients with negative liquid biopsy test, B0, had improved survival compared to patients with a positive liquid biopsy test, B1 (p&amp;lt;0.005). In our cohort, TNM staging alone did not show a significant survival difference between stage II and III (p = 0.19). Whereas TNMB staging showed a significant survival difference between stage II, III and IV. Conclusions: While TNM stage remains a major prognostic factor for NSCLC patients, it fails to incorporate molecular data which has a critical impact on management and prognosis. We designed a novel method to analyze molecular data obtained by liquid biopsy to identify significant gene mutations and gene magnitude of VAF that impacts survival prediction. In our cohort, we showed 4 impact genes which we used to create a new TNMB staging system which led to improved survival prediction compared to TNM alone. Acknowledgments: Research partly supported by NIH T32 Grant (T32CA247819) and Cancer Center Support Grant (P30CA012197) Reference: (1) Yang M, Forbes ME, Bitting RL, O'Neill SS, Chou PC, Topaloglu U, Miller LD, Hawkins GA, Grant SC, DeYoung BR, Petty WJ, Chen K, Pasche BC, Zhang W. Incorporating Blood-based Liquid Biopsy Information into Cancer Staging: Time for a TNMB System? Ann Oncol. 2018 Feb 1;29(2):311-323. Citation Format: Lauren Schmalz, Cetin Urtis, Liang Liu, Elizabeth Forbes, Fang-Chi Hsu, Nury Steuerwald, Alberto de Hoyos, Thomas Lycan, Jimmy Ruiz, William Petty, Wei Zhang. Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5065.

Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA-IIIA.

The 9th edition of tumour-node-metastasis (TNM) staging for lung cancer was announced by Prof Hisao Asamura at the 2023 World Conference on Lung Cancer in Singapore. The purpose of this study was to externally validate and compare the latest staging of lung cancer. We collected 19193 patients with stage IA-IIIA non-small cell lung cancer (NSCLC) who underwent lobectomy from the Surveillance, Epidemiology and End Results database. Survival analysis by TNM stages was compared using the Kaplan-Meier method and further analysed using univariable and multivariable Cox regression analyses. Receiver operating characteristic curves were used to assess model accuracy, Akaike information criterion, Bayesian information criterion and consistency index were used to compare the prognostic, predictive ability between the current 8th and 9th edition TNM classification. The 9th edition of the TNM staging system can better distinguish between IB and IIA patients on the survival curve (P < 0.0001). In both univariable and multivariable regression analysis, the 9th edition of the TNM staging system can differentiate any 2 adjacent staging patients more evenly than the 8th edition. The 9th and the 8th edition TNM staging have similar predictive power and accuracy for the overall survival of patients with NSCLC [TNM 9th vs 8th, area under the curve: 62.4 vs 62.3; Akaike information criterion: 166182.1 vs 166131.6; Bayesian information criterion: 166324.3 vs 166273.8 and consistency index: 0.650 (0.003) vs 0.651(0.003)]. Our external validation demonstrates that the 9th edition of TNM staging for NSCLC is reasonable and valid. The 9th edition of TNM staging for NSCLC has near-identical prognostic accuracy to the 8th edition.

Development and validation of a nomogram for predicting the impact of tumor size on cancer-specific survival of locally advanced renal cell carcinoma: a SEER-based study.

This study was aimed to integrate tumor size with other prognostic factors into a prognostic nomogram to predict cancer-specific survival (CSS) in locally advanced (≥pT3a Nany M0) renal cell carcinoma (RCC) patients. Based on the Surveillance, Epidemiology, and End Results (SEER) database, 10,800 patients diagnosed with locally advanced RCC were collected. They were randomly divided into a training cohort (n = 7,056) and a validation cohort (n = 3,024). X-tile program was used to identify the optimal cut-off value of tumor size and age. The cut-off of age at diagnosis was 65 years old and 75 years old. The cut-off of tumor size was 54 mm and 119 mm. Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 1-, 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), area under the Subject operating curve (AUC) and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 8th edition) were compared. 10 variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) indicated satisfactory ability of the nomogram. Compared with the AJCC 8th edition of TNM stage, DCA showed that the nomogram had improved performance. We developed and validated a nomogram for predicting the CSS of patients with locally advanced RCC, which was more precise than the AJCC 8th edition of TNM staging system.

Long-term outcomes of intensity-modulated radiotherapy in limited-stage small-cell lung cancer classified according to AJCC 8th tumor node metastasis staging system.

The objective of this study was to assess treatment outcomes of intensity-modulated radiotherapy with concomitant chemotherapy and to identify prognostic factors on survival in patients with limited-stage small-cell lung cancer. A retrospective analysis was conducted on a cohort of seventy-two patients who received curative treatment between December 2011 and January 2023. Several clinical and biochemical parameters were examined as potential prognostic factors. The median age was 63 years, and 79% of them were males. Concomitant chemotherapy was administered in 83% of patients. Prophylactic cranial irradiation was applied in 61% of the cohort. Two and five-year overall survival (OS), disease-free survival (DFS), and local relapse-free survival (LRFS) rates were 50% and 25%, 38% and 24%, and 44% and 25%, respectively. Univariate analysis revealed that older age, comorbid lung disease, advanced tumor-node-metastasis (TNM) stage, radiotherapy (RT) alone, and the absence of prophylactic cranial irradiation (PCI) were adverse factors affecting OS. The advanced TNM stage emerged as a significant prognostic factor for LRFS and DFS, with a notable trend toward affecting OS. The TNM staging system is of significance in cases classified as limited-stage small-cell lung cancer due to its prognostic implications. Our results suggest that patients with more advanced TNM stage exhibit less favorable treatment outcomes, which may require individual tailoring of new systemic therapies.

A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II-III colorectal cancer.

With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II-III CRC and construct a nomogram to predict patient survival. A total of 3,016 elderly CRC patients with stage II-III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups. The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001). We constructed a prediction nomogram for stage II-III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Nomograms predict survival in elderly women with triple-negative breast cancer: A SEER population-based study.

The effective treatment of breast cancer in elderly patients remains a major challenge. To construct a nomogram affecting the overall survival of triple-negative breast cancer (TNBC) and establish a survival risk prediction model. A total of 5317 TPBC patients with negative expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) who were diagnosed and received systematic treatment from 2010 to 2015 were collected from the American Cancer Surveillance, Epidemiology and End Results (SEER) database. They were randomly divided into training set (n= 3721) and validation set (n= 1596). Univariate and multivariate Cox regression analysis were used to identify prognostic features, and a nomogram was established to predict the probability of 1-year, 3-year and 5-year OS and BCSS. We used consistency index (C-index), calibration curve, area under the curve (AUC) and decision curve analysis (DCA) to evaluate the predictive performance and clinical utility of the nomogram. The C-indices of the nomograms for OS and BCSS in the training cohort were 0.797 and 0.825, respectively, whereas those in the validation cohort were 0.795 and 0.818, respectively. The receiver operating characteristic (ROC) curves had higher sensitivity at all specificity values as compared with the Tumor Node Metastasis (TNM) system. The calibration plot revealed a satisfactory relationship between survival rates and predicted outcomes in both the training and validation cohorts. DCA demonstrated that the nomogram had clinical utility when compared with the TNM staging system. This study provides information on population-based clinical characteristics and prognostic factors for patients with triple-negative breast cancer, and constructs a reliable and accurate prognostic nomogram.

The Relationship between Cancer Stage, Selected Immunological Parameters, Epstein-Barr Virus Infection, and Total Serum Content of Iron, Zinc, and Copper in Patients with Laryngeal Cancer.

(1) Background: the purpose of the study was to assess the relationship between cancer stage, selected immunological parameters, Epstein-Barr virus (EBV) infection, and total serum content of iron, zinc, and copper in patients with laryngeal cancer (LC). (2) Methods: serum Fe, Zn, and Cu were measured in 40 LC patients and 20 controls. Immunophenotyping of peripheral blood lymphocytes was performed by flow cytometry using fluorescent antibodies against CD3, CD4, CD8, CD19, CD25, CD69, and PD-1. Tumor and lymph node lymphocytes were analyzed by flow cytometry. EBV DNA was quantified by real-time PCR, targeting the EBNA-1 gene. Associations between serum elements, immune markers, and cancer grade/stage were evaluated using ANOVA and appropriate nonparametric tests. (3) Results: levels of Fe, Cu, and Zn were lower, while Cu/Zn was statistically higher, in patients with LC than in the control group. Correlation analysis showed a statistically significant association between the levels of these elements and parameters of the TNM (Tumor, Node, Metastasis) staging system, immunophenotype, and the amount of EBV genetic material in patients with LC who survived for more than 5 years. (4) Conclusion: the results suggest that the total serum levels of the determined micronutrients may significantly affect the immunopathogenesis and progression of LC.

Prognostic value of lymph node-to-primary tumor ratio of PET standardized uptake value for nasopharyngeal carcinoma: a recursive partitioning risk stratification analysis.

Induction chemotherapy (IC) combined with concurrent chemoradiotherapy has become the standard treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Data on the prognostic value of the lymph node-to-primary tumor ratio (NTR) of positron emission tomography (PET) standardized uptake value (SUV) for patients treated with IC were limited. To evaluate the prognostic value of the SUV NTR for patients with LA-NPC treated with IC. In all, 467 patients with pretreatment 18F-fluorodeoxyglucose PET/computed tomography (CT) scans between September 2017 and November 2020 were retrospectively reviewed. The receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value of SUV NTR. Kaplan-Meier method was used to evaluate survival rates. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The optimal cutoff value of SUV NTR was 0.74. Multivariate analyses showed that SUV NTR and overall stage were independent predictors for distant metastasis-free survival (DMFS) and regional recurrent-free survival (RRFS). Therefore, an RPA model based on the endpoint of DMFS was generated and categorized the patients into three distinct risk groups: RPA I (low risk: SUV NTR < 0.74 and stage III), RPA II (medium risk: SUV NTR < 0.74 and stage IVa, or SUV NTR ⩾ 0.74 and stage III), and RPA III (high risk: SUV NTR ⩾ 0.74 and stage IVa), with a 3-year DMFS of 98.9%, 93.4%, and 84.2%, respectively. ROC analysis showed that the RPA model had superior predictive efficacy than the SUV NTR or overall stage alone. SUV NTR was an independent prognosticator for distant metastasis and regional recurrence in locoregionally advanced NPC. The RPA risk stratification model based on SUV NTR provides improved DMFS and RRFS prediction over the eighth edition of the TNM (Tumor Node Metastasis) staging system.

Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer.

Background: Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. Methods: In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. Results: The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. Conclusions: The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.

Negative prognostic impact of tumor deposits in stage III colorectal cancer patients.

The prognostic value of tumor deposits (TDs) in stage III colorectal cancer (CRC) patients is poorly described based on the current tumor node metastasis (TNM) stage system. Based on the data from the Surveillance, Epidemiology, and End Result (SEER) database between 2010 to 2020 and local hospital between 2006 to 2022, the clinicopathological features of stage III CRC patients with TDs were screened by Chi-square test. Kaplan-Meier curves were performed to describe the significant difference in overall survival (OS) among the different groups, and log-rank tests were used to compare the cumulative survival distributions. Patients with TDs exhibited more aggressive tumors, characterized by advanced T staging (T3&T4), N staging (N2), perineural invasion, and more advanced TNM stage. The presence of TDs was identified as a negative prognostic factor in stage III CRC patients, with the co-existence of TDs and lymph node metastasis associated the poorest prognosis. A pairwise comparison revealed no statistically significant difference between TD+N1a/b and N1c groups, while the OS of TD-LN+ (TD- N1a/b) patients was the most favorable within the N1 stage. Notably, patients with a single lymph node positive had a significantly better OS than those with a single TD positive. The presence of tumor deposits was a negative prognostic factor in stage III colorectal cancer patients, and the significance of tumor deposits was underestimated in the current TNM staging system.

A predictive model and rapid multi-dynamic algorithm developed based on tumor-stroma percentage in gastric cancer: a retrospective, observational study.

Tumor-stroma percentage (TSP) is a prognostic risk factor in numerous solid tumors. Despite this, the prognostic significance of TSP in gastric cancer (GC) remains underexplored. Through the development of a personalized predictive model and a semi-automatic identification system, our study aimed to fully unlock the predictive potential of TSP in GC. We screened GC patients from Shanghai General Hospital (SGH) between 2012 and 2019 to develop and validate a nomogram. Univariate and multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic factors influencing the prognosis for GC patients. The nomogram was further validated externally by using a cohort from Bengbu Medical College (BMC). All patients underwent radical gastrectomy, with those diagnosed with locally advanced GC receiving adjuvant chemotherapy. The primary outcome measured was overall survival (OS). The semi-automatic identification of the TSP was achieved through a computer-aided detection (CAD) system, denoted as TSP-cad, while TSP identified by pathologists was labeled as TSP-visual. A total of 813 GC patients from SGH and 59 from BMC were enrolled in our study. TSP-visual was identified as an adverse prognostic factor for OS in GC and was found to be associated with pathological Tumor Node Metastasis staging system (pTNM) stage, T stage, N stage, perineural invasion (PNI), lymphovascular invasion (LVI), TSP-visual, tumor size, and other factors. Multivariate Cox regression using the training cohort revealed that TSP-visual (hazard ratio [HR], 2.042; 95% confidential interval [CI], 1.485-2.806; P < 0.001), N stage (HR, 2.136; 95% CI, 1.343-3.397; P = 0.010), PNI (HR , 1.791; 95% CI, 1.270-2.526; P = 0.001), and LVI (HR, 1.482; 95% CI, 1.021-2.152; P = 0.039) were independent predictors. These factors were incorporated into a novel nomogram, which exhibited strong predictive accuracy for 5-year OS in the training, internal validation, and external validation cohorts (area under the curve = 0.744, 0.759, and 0.854, respectively). The decision curve analysis of the nomogram and concordance indexes across the three cohorts outperformed the traditional pTNM (P < 0.05). Additionally, TSP-cad assessment using a rapid multi-dynamic algorithm demonstrated good agreement with TSP-visual. The novel nomogram based on TSP could effectively identify individuals at risk of a poor prognosis among patients with GC. TSP-cad is anticipated to enhance the evaluation process of TSP.