AJCC Stage Research Articles

Clinical and molecular prognostic nomograms for patients with papillary renal cell carcinoma

ObjectiveTo summarize the clinicopathological characteristics and prognostic factors of papillary renal cell carcinoma (pRCC) and to construct clinical and molecular prognostic nomograms using existing databases.MethodsClinical prognostic models were developed using the Surveillance, Epidemiology, and End Results (SEER) database, while molecular prognostic models were constructed using The Cancer Genome Atlas (TCGA) database. Cox regression and LASSO regression were employed to identify clinicopathological features and molecular markers related to prognosis. The accuracy of the prognostic models was assessed using ROC curves, C-index, decision curve analysis (DCA) curves, and calibration plots.ResultsIn the 2004–2015 SEER cohort, Cox regression analysis revealed that age, grade, AJCC stage, N stage, M stage, and surgery were independent predictors of overall survival (OS) and cancer-specific survival (CSS) in pRCC patients. ROC curves, C-index, and DCA curves indicated that the prognostic nomogram based on clinical independent predictors had better predictive ability than TNM staging and SEER staging. Additionally, in the TCGA cohort, M stage, clinical stage, and the molecular markers IDO1 and PLK1 were identified as independent risk factors. The prognostic nomogram based on molecular independent risk factors effectively predicted the 3-year and 5-year OS and CSS for pRCC patients.ConclusionsThe clinical and molecular nomograms constructed in this study provide robust predictive tools for individualized prognosis in pRCC patients, offering better accuracy than traditional staging systems.

Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers

BackgroundTumor mutation burden (TMB) has been considered a biomarker for utilization of immune checkpoint inhibitors(ICIs), but whole exome sequencing(WES) and cancer gene panel(CGP) based on next generation sequencing for TMB detection are costly. Here, we use transcriptome data of TCGA to construct a model for TMB prediction in gastrointestinal tumors.MethodsTranscriptome data, somatic mutation data and clinical data of four gastrointestinal tumors from TCGA, including esophageal cancer (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Using R, we performed visual analysis of somatic mutation data, differentially expressed genes (DEGs) function enrichment analysis, gene set enrichment analysis (GSEA), and estimated TMB value in clinic. Finally, a deep neural network (DNN) model was constructed for TMB prediction.ResultsVisualization of somatic mutation data summarized the classification of mutation, frequency of each mutation type, and top-mutated genes. GSEA showed the enrichment of CD4+/CD8+ T cells in the high TMB group and the activation of tumor suppressing pathways. Single-sample GSEA (ssGSEA) manifested that the high-TMB group had higher level of multiple immune cells infiltration. In addition, distribution of TMB was related to clinical parameters.Like age, M stage, N stage, AJCC stage, and overall survival(OS). After model optimization using genetic algorithm, in the training set, validation set, and testing set, the Pearson relevance coefficient r between predicted values and actual values reaches 0.98, 0.82, and 0.92, respectively; the coefficient of determination R2 is 0.95, 0.82, and 0.7, respectively.ConclusionTMB correlates with clinicopathological parameters in gastrointestinal carcinoma, and patients with high TMB have higher levels of immune infiltration. In addition, the DNN model based on 31 genes predicts TMB of gastrointestinal tumors in a high accuracy.

Soft tissue tumor imaging in adults: European Society of Musculoskeletal Radiology-Guidelines 2024: imaging immediately after neoadjuvant therapy in soft tissue sarcoma, soft tissue tumor surveillance, and the role of interventional radiology.

An update of the first European Society of Musculoskeletal Radiology (ESSR) consensus on soft tissue tumor imaging in 2015 became necessary due to technical advancements, further insights into specific entities, and the revised WHO classification (2020) and AJCC staging system (2017). The third part of the revised guidelines covers algorithms and techniques beyond initial imaging: (1) Imaging after neoadjuvant therapy in soft tissue sarcoma, (2) sarcoma surveillance, and (3) special aspects, including surveillance of non-malignant entities and the role of interventional radiology. A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by level of agreement (0 to 10) during two iterative rounds that could result in either 'group consensus,' 'group agreement,' or 'lack of agreement.' The three sections contain 47 statements with comments. Group consensus was reached in 91.5%, group agreement in 6.4%, lack of agreement in 2.1%. In sarcoma, imaging immediately after neoadjuvant therapy is pivotal for determining the therapy effects and for resection-planning; surveillance should include imaging at fixed grade- and type-dependent intervals. In general, MRI is the method of choice for loco-regional surveillance of soft tissue sarcomas, and chest CT to assess metastatic disease. Interventional radiology has a role, especially in oligometastatic disease, palliative tumor control and local recurrences. Strategies for standardized soft tissue tumor imaging regarding therapy control, surveillance, and useful interventional procedures are provided. Question An ESSR consensus update on soft tissue tumor imaging regarding surveillance became necessary due to technical advancements, further entity-specific insights, and revised WHO- and AJCC-classifications. Findings Imaging immediately after neoadjuvant therapy in soft tissue sarcoma is pivotal. Post-therapeutic surveillance should include imaging at regular intervals, stratified for tumor grade and type. Clinical relevance The updated ESSR soft tissue tumor imaging guidelines aim to provide best practice expert consensus for standardized imaging, to support radiologists in their decision-making, and to improve examination comparability, both in individual patients and in future studies on individualized strategies.

Individualized Prediction for Risk of Recurrence in Stage I/II Melanoma Patients With Negative Sentinel Lymph Node.

Despite the favorable prognosis of AJCC stage I/II melanoma patients, up to 20%-30% will develop metastases. Our objective is to predict long-term risk (probability) of recurrence in early-stage melanoma patients. A Risk Score to predict long-term recurrence was developed using Cox regression based on 2668 patients. Five clinicopathological risk factors were included. The association of the Risk Score with the risk of recurrence was evaluated using parametric models (exponential, Weibull, and Gompertz models) and compared to the Cox model using the Akaike information criterion. The discrimination of the model was measured by time-dependent ROC analyses. A calibration curve was used to evaluate the agreement between predicted and observed recurrence probabilities. The bootstrap adjusted C-index was 0.76 (95% CI, 0.74-0.79) overall and 0.87 (0.83-0.90) and 0.82 (0.78-0.85) at one and two years, respectively, and then remained above 0.70 up to 20 years. The Gompertz model for prediction of survival from the Risk Score showed the best performance and displayed good agreement with the Kaplan-Meier curves. The calibration curve of the Gompertz model showed a good agreement between predicted and observed 2-, 5-, and 10-year risk of recurrence. Population-level analysis demonstrated a significant association of Risk Score with risk of recurrence, with 10-year risks of recurrence of 4.5%, 13.0%, and 33.7% in the first, second, and third tertiles, respectively. We developed a Risk Score to predict long-term risk of recurrence for early-stage melanoma patients. A Gompertz survival model fit to the Risk Score allows for individualized prediction of time-dependent recurrence risk.

Nomograms for Predicting Overall and Cancer-Specific Survival Among Second Primary Endometrial Cancer in Primary Colorectal Carcinoma Patients.

Endometrialcancer (EC)isone of the most frequentgynecologic cancers, approximately 20%ofpatients are regarded as high-risk with poor prognosis. However, moredetailsof patients with second primary endometrialcancer (SPEC) after colorectal cancer (CRC) remain poorly understood. We therefore proposed to construct twonomogramsto predict 3- and5-year overall survival (OS) and cancer-specific survival (CSS) rates to facilitateclinicalapplication. A total of 1631 participants wereidentified inthe SEER databasefrom1973to2020. We constructed and validated the nomograms for predicting OS and CSS. Thereceiver operating characteristic curves,calibrationplot, decision curve analysis, C-index, net reclassification improvement, and integrated discrimination improvement were applied to evaluatethe predictive performance. Finally, the Prognosticindex was calculated and usedfor riskstratificationof Kaplan-Meiersurvivalanalysis based on different treatmentoptions. Nomograms of OS and CSS were formulated based on theindependentprognosticfactors utilizingthetrainingset. The3- and 5- years of OS nomogram demonstratedgooddiscrimination (AUC = 0.840 and 0.829, respectively), well-calibrated power,and excellentclinicaleffectiveness. Ournomograms of predicting OS and CSS had a concordance index of 0.801 and 0.866 compared with 0.676 and 0.746 for the AJCC staging system,and more importantly,demonstrated a better forecast accuracy. Chemoradiotherapy displayed a significant survival benefit in the high-risk groups, but proceeding to surgery plus chemotherapy showed a favorable survival for the low groups based on all patients. We developed andinternallyvalidated multivariable models that predict OS and CSS risk of SPEC in patients with a CRC to helpclinicians makeapplicable clinical decisions for patients.

Efficacy of neoadjuvant therapy and lymph node dissection in advanced gallbladder cancer without distant metastases: a SEER database analysis.

To investigate the effectiveness of neoadjuvant therapy and lymph node dissection(LND) on overall survival (OS) in patients with stage III/IV gallbladder cancer without distant metastases. Data from 101 patients who received neoadjuvant therapy followed by surgery combined with adjuvant chemotherapy, and 1412 patients who received direct surgical treatment followed by adjuvant chemotherapy, were collected from the SEER database from 2004 to 2020. Patients were divided into group A (neoadjuvant therapy) and group B (direct surgery) based on the treatment modality. A total of 202 cases were obtained after propensity score matching, with 101 cases in each group (A and B). Cox unifactorial and multifactorial analyses were performed to identify independent risk factors for patients with advanced cholecystic carcinoma, and the Kaplan-Meier method was used to analyze overall survival (OS). The Cox proportional hazards model was used to investigate the effect of different subgroups on OS in both patient groups. Further survival analyses were conducted to determine whether lymph node dissection(LND) was beneficial for patients receiving neoadjuvant therapy for gallbladder cancer. Cox univariate analysis showed that marital status, AJCC stage, number of LND, tumor size, and treatment modality were associated with OS (P<0.05). Cox multifactorial regression analysis indicated that AJCC stage, LND, tumor size, and treatment modality were independent risk factors for OS in patients with non-metastatic advanced gallbladder cancer (P<0.05). Survival curves demonstrated that the OS in group A was longer than in group B (median OS: 30 months vs. 14 months, P<0.001). Subgroup analysis indicated that neoadjuvant therapy had a consistent effect on the OS of patients with advanced gallbladder cancer, improving both survival time and outcomes. Survival curves indicated that lymph node dissection was not significant in group A patients (p>0.05) but was significant in group B (p<0.05). Neoadjuvant therapy can improve the OS of patients with non-metastatic stage III/IV gallbladder cancer and is an independent risk factor affecting prognosis; however, the significance of lymph node dissection in these patients still needs further study.

SLITRK2 as a prognostic and immunological biomarker in gastric cancer

BackgroundSLIT and NTRK-like protein 2 (SLITRK2) encodes a transmembrane protein that regulates neurite outgrowth. Some studies have demonstrated that SLITRK2 overexpressed in glioma. But the expression pattern, prognostic value and immunologic function of SLITRK2 in tumors remains unknown.MethodsThe expression pattern of SLITRK2 among pan-cancers was examined through the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). We analyzed the association between SLITRK2 expression level and tumor stage among pan-cancers. Kaplan–Meier survival analysis was utilized to investigate the prognostic relevance of SLITRK2 across 33 different types of cancers. Moreover, the correlations among SLITRK2 expression, immune cell infiltration, immunomodulatory related genes, tumor mutation burden (TMB), microsatellite instability (MSI) were evaluated. The relationship between SLITRK2 expression and crucial genes mutations was also illustrated. By using tissue microarray (TMA), the expression of SLITRK2 in 89 paired gastric cancer (GC) tissues was investigated.ResultsOur study indicated that SLITRK2 expression varied across cancers. Elevated SLITRK2 expression was positively related to advanced tumor stage, poor overall survival (OS) and reduced disease-free survival (DFS). Bioinformatic analyses underscore SLITRK2’s role in immune response, with its expression significantly tied to immune cell infiltration and marker expression. Based on TMA data, SLITRK2 expression level was positively associated with differentiation, lymph node metastasis, AJCC stage, TNM stage, and poor survival outcome in GC patients.ConclusionOur findings provided that SLITRK2 may function as a biomarker by regulating immune cell infiltration. In addition, we verified that high SLITRK2 expression was correlated with poor prognosis in GC.

Analysis of METTL14 expression in pancreatic cancer and adjacent tissues and its prognostic value for patient outcomes

This study aims to analyze the differential expression of METTL14 in pancreatic cancer (PC) tissues and adjacent normal tissues, and its correlation with clinical outcomes. According to the inclusion and exclusion criteria, a total of 80 patients diagnosed in our hospital from January 2021 to January 2023 were chosen as research subjects. RTQ-PCR has detected the mRNA level expression of METTL14 in cancer and para-cancerous tissues. Immunohistochemistry was used to detect the protein expression of METTL14 in cancer and para-cancerous tissues. To compare the relationship between METTL14 expression and clinicopathological parameters in different PC patients. Kaplan–Meier survival analysis of the relationship between METTL14 expression in PC tissues and patient survival prognosis. The Multifactor COX model evaluates factors affecting the prognosis of PC. The expression level of METTL14 mRNA in PC tissues was 5.51 ± 0.35 (kDa), and the positive rate of METTL14 protein expression in PC tissues of all patients was 73.75 (59/80). Tumor location (P = 0.012), tumor differentiation degree (P = 0.028), tumor AJCC stage (P = 0.000), and lymph node metastasis (P = 0.000) were significantly related to the positive rate of METTL14 protein expression in PC tissue. Follow-up results showed that among 80 patients, 63 died. The three-year survival rate of the METTL14 positive group was 13.56% (8/59), and the three-year survival rate of the negative group was 42.86% (9/21). The difference in the three-year survival rate between METTL14 positive and negative expression groups was statistically significant (P = 0.031). Multivariate COX regression analysis results showed that METTL14 was positive (OR 2.797, 95% CI 1.233–5.877), tumor AJCC stage II–III (OR 1.628, 95% CI 1.435–3.859) and lymph node metastasis (OR 1.733, 95% CI 1.122–2.372) were substantive risk factors for poor prognosis in patients with PC. METTL14 expression increases in PC tissue, which is related to tumor AJCC stage, tumor differentiation, and lymph node metastasis, and can be evaluated in the survival prognosis of patients with PC.

Systematic review of risk prediction tools for primary cutaneous melanoma outcomes and validation of sentinel lymph node positivity prediction in a UK tertiary cohort

BackgroundIt is difficult for clinicians to make predictions for cancer progression or outcomes based on AJCC staging for individual patients. Models individualising risk prediction for clinical outcomes are developed using patient level data, advanced statistical techniques, and artificial intelligence.MethodsA systematic search identified cutaneous melanoma prognostic prediction tools published between January 1985–March 2023. Population comparisons of key clinico-pathological variables, external prediction of receiver operating characteristics and calibration analysis are applied to an unselected group of patients undergoing sentinel lymph node biopsy in a UK University hospital setting (n = 1564).ResultsTwenty-nine models were identified which predicted survival, disease recurrence or sentinel lymph node positivity (Internal validation n = 19 and external validation n = 14). 3 out of 7 tools for sentinel node positivity were contemporaneous with available characteristics for external validation. External validation of models by Lo et al. Friedman et al. & Bertolli et al. highlighted good discriminative performance (AUC 68.1% (64.5–71.8%), 77.1% (66.8–85.7%) & 68.6% (63.3–74.1%) respectively) but were sub-optimally calibrated for the UK patient cohort (Calibration intercept & slope Friedman: −4.01 & 32.92, Lo: −1.17 & 0.44, Bertolli: −2.75 & 4.88).ConclusionsThis work highlights the complexity of predictive modelling and the rigorous validation process necessary to ensure accurate predictions. Our search highlights a tendency to focus on discriminative performance over calibration, and the possibility for inconsistent predictions when tools are applied to populations with differing characteristics.

Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70 %) and validation (30 %) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

Presentation and outcome of Gastric Cancer at a Tertiary Care Center

Introduction: Gastric cancer is the fifth most common cancer globally and the fourth leading cause of cancer-related deaths. Early-stage disease can be managed with endoscopic therapy or surgery, while advanced gastric cancer requires a multimodal treatment approach, including extensive lymphadenectomy. This study aims to evaluate the clinicodemographic profile and treatment outcomes of gastric cancer patients and to establish an effective therapeutic strategy. Methods: This retrospective descriptive cross-sectional study was conducted in the Department of Surgery at Shree Birendra Hospital. It included patients with gastric malignancies who had complete medical records. Data on patient history, AJCC staging, operative details, and postoperative findings were analyzed. Surgical choices were based on the clinical indication in each case. Clinicopathological characteristics and surgical outcomes were compared across tumor stages. Data were analyzed using Statistical Package for Social Sciences (SPSS) version 24. Results: The study included 90 patients (62 males, 28 females) aged between 23 and 82 years. The most common clinical presentations were abdominal pain (58%), gastric outlet obstruction (18%), lump (5%), anemia (5%), and peritonitis (4%). Adenocarcinoma was the predominant histological type (87.78%). Curative surgery was performed on 73 patients (81.11%), while 17 patients (18.89%) underwent palliative procedures. Morbidity occurred in 16 patients (17.7%), primarily due to chest complications and sepsis, and mortality was recorded in 5 patients (5.5%). Conclusion: This study highlights the predominance of locally advanced gastric cancer in males, with distal gastric cancer and well-differentiated adenocarcinoma being the most common. Chest complications and sepsis were the leading causes of morbidity and mortality. No patients received perioperative chemotherapy, underscoring the need for updated treatment protocols to improve outcomes.

Improving Prognostic Value in Invasive Triple Negative Breast Cancer Through a Combined Nomogram Approach

ObjectivesTo investigate the potential prognostic value of ultrasound (US) features in conjunction with pathological markers and to develop a preliminary working model for predicting poor outcomes in patients with invasive triple-negative breast cancer (TNBC). MethodsFrom January 2012 to December 2018, we enrolled 209 TNBC patients treated with standard therapy, systematically gathered data on US parameters, stromal tumor-infiltrating lymphocytes (TILs), lymphovascular invasion (LVI) status, and other relevant information, and recorded follow-up data. A nomogram combining AJCC staging with US score, stromal TILs, and LVI was constructed and validated to predict poor outcomes, defined as recurrence or death, in patients with invasive TNBC. ResultsThe US score of 4 was best related to poor outcomes in patients with TNBC (HR 3.87, p = 0.015). In the training set, the nomogram had a considerably greater prognostic value [area under the curve (AUC), 0.74 vs. 0.64, p=0.045] than AJCC staging alone, and it was comparable to that of the validation set (AUC, 0.71 vs. 0.63, p=0.804). An acceptable consistency between the nomogram-predicted and actual survival probabilities was found both in the training and validation sets, with Brier scores of 0.15 and 0.13, respectively. ConclusionsThe incorporation of AJCC stage with US score, stromal TILs, and LVI improved the model performance for predicting poor outcomes in patients with invasive TNBC compared to routine AJCC staging alone. MicroAbstractA nomogram was developed to predict the disease-free survival of invasive triple-negative breast cancer (TNBC) patients who had received standard therapy. The nomogram was constructed using ultrasound features and pathological variables, including stromal tumour-infiltrating lymphocytes as well as lymphovascular invasion, in conjunction with the AJCC stage. The nomogram was developed using data from 209 TNBC patients. The combined model improved the predictive performance compared to routine AJCC staging, and might help to identify patients at high risk of poor outcomes.

Plasma COL10A1 Level, a Potential Diagnostic and Prognostic Biomarker for Pancreatic Ductal Adenocarcinoma.

COL10A1 expression was up-regulated and could promote tumor development in pancreatic cancer. As a secreted protein, plasma COL10A1 level was proven to have certain diagnostic efficacy in gastric cancer, breast cancer, and colorectal cancer. It is still unknown whether it has a biomarker role for pancreatic cancer. To explore and analyze the diagnostic and prognostic value of plasma COL10A1 level in pancreatic ductal adenocarcinoma (PDAC). The RNA-seq dataset of PDAC from The Cancer Genome Atlas (TCGA) and six expression profiling microarray datasets from Gene Expression Omnibus (GEO) were downloaded to analyze the expression of COL10A1 in tissues. Thirty-six patients with PDAC and eighteen healthy volunteers were enrolled to measure COL10A1 levels in tissues and plasmas, and the relationship between clinical characteristics and the COL10A1 levels was analyzed. The diagnostic and prognostic efficacy of plasma COL10A1 levels were calculated. Aspects of COL10A1 expression level in tissues, COL10A1 expression was significantly higher in PDAC tissue than adjacent normal tissue. The expression of COL10A1 was correlated with T, M, and AJCC stages. Patients with high COL10A1 expression had worse recurrence-free survival (RFS) and overall survival (OS) than those with low expression. Aspects of COL10A1 expression levels in plasma, its diagnostic area under the curve (AUC) for PDAC was 0.926 (95% CI 0.853-0.999), diagnostic sensitivity was 81% (95% CI 64-92%), and specificity was 100% (95% CI 81-100%). The time-dependent AUCs at 1-year and 3-year were 0.71 (95% CI 0.51-0.90) and 0.74 (95% CI 0.48-1.00), respectively. In PDAC, plasma COL10A1 levels showed certain diagnostic and prognostic efficacy. COL10A1 may be a diagnostic and prognostic biomarker for PDAC and play a role in liquid biopsy of this disease.

Clinical Outcomes, Patterns of Failure, and Salvage Therapies of a Large Modern Cohort of Patients With Anal Squamous Cell Carcinoma Treated With Definitive-Intent Intensity-Modulated Radiation Therapy

PurposePatterns of failure and salvage therapy options for patients with anal squamous cell carcinoma (ASCC) who recur after definitive-intent intensity-modulated radiotherapy (IMRT) with concurrent chemotherapy are not well described. Patients and MethodsWe identified consecutive patients with ASCC treated with definitive-intent IMRT between July 2005 and December 2019. Relevant patient and tumor parameters, disease outcomes (locoregional failure (LRF), distant failure (DF), progression-free survival (PFS), colostomy-free survival (CFS), and overall survival (OS)), patterns of failure, and salvage therapies were collected. Failures were analyzed by competing risks methods, whereas survival endpoints were estimated by Kaplan-Meier method. Univariate and multivariate analyses were performed. Landmark analyses were conducted by considering whether patients had LRF within 12 months from completing IMRT. Results375 patients were identified with a median follow-up of 6 years. Stage breakdown was 15%, 23%, and 62% for AJCC stage 0-I, II, and III, respectively. Six-year rates of LRF, DF, PFS, CFS, and OS were 12%, 13%, 73%, 76%, and 80%, respectively. Disease recurred in 74 patients. Among the 45 patients with LRF, 39 (87%) failed within the anorectum, with 25 anal canal, 6 anal margin, and 8 rectal recurrences. Only 4 (9%) patients had isolated nodal failure. Patients experiencing LRF had worse six-year OS than patients without LRF (44% versus 86%, P<0.0001). Approximately 30% of patients who underwent salvage therapy were alive ten years after recurrence, compared with none of the patients who were managed with chemotherapy alone or best supportive care. ConclusionsThis large ASCC cohort managed with definitive-intent IMRT demonstrated excellent rates of locoregional control and survival. Isolated regional nodal failures were uncommon, whereas the majority of LRFs occurred within the anorectum, despite dose escalation by tumor stage. We observed poor outcomes for patients experiencing locoregional disease recurrence, even after aggressive salvage treatment.

Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer.

The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone. Patients with either histological or radiological diagnoses of lung cancer were included in this study. The patient cohort comprised 1864 patients diagnosed from 2016 to 2021. A total of 25 variables were considered as potential risk factors. These variables have been identified in previous studies as independent risk factors for bone metastasis. Treatment methods for lung cancer were taken into account during model development. The outcome variable was binary, (presence or absence of bone metastasis) with follow-up until death or 12-month survival, whichever is the sooner. Results showed that American Joint Committee on Cancer staging, the use of EGFR inhibitor, age, T-staging, and lymphovascular invasion were the five input features contributing the most to the model algorithm. High AJCC staging (OR 1.98; p < 0.05), the use of EGFR inhibitor (OR 6.14; p < 0.05), high T-staging (OR 1.47; p < 0.05), and the presence of lymphovascular invasion (OR 4.92; p < 0.05) increase predicted risk of bone metastasis. Conversely, older age reduces predicted bone metastasis risk (OR 0.98; p < 0.05). The machine learning model developed in this study can be easily incorporated into the hospital's Clinical Management System so that input variables can be immediately utilized to give an accurate prediction of bone metastatic risk, therefore informing clinicians on the best treatment strategy for that individual patient.

Impact of 8th edition AJCC staging changes on sentinel lymph node biopsy practices in melanoma

Impact of 8th edition AJCC staging changes on sentinel lymph node biopsy practices in melanoma

Prognostic Nomogram for Predicting Survival in Asian Patients With Small-Cell Lung Cancer: A Comprehensive Population-Based Study and External Verification.

The incidence of small cell lung cancer (SCLC) among Asian patients is on the rise. Nevertheless, there remains a deficiency in precise prognostic models tailored to the specific needs of this patient population. It is imperative to develop a novel nomogram aimed at forecasting the prognosis of Asian SCLC patients. The SEER database supplied data on 661 Asian SCLC patients, who were then divided into training and internal validation sets through a random selection process. In addition, we identified 212 patients from a Chinese medical institution for the purpose of creating an external validation cohort. To forecast survival, we employed both univariate and multivariate analyses. The performance of our nomogram was assessed through calibration plots, the concordance index (C-index), and decision curve analysis (DCA). Five independent prognostic factors were determined and integrated into the nomogram. C-index values for the training and internal validation cohorts were 0.774 (95% confidence interval [CI] = 0.751-0.797) and 0.731 (95%CI = 0.690-0.772), respectively. In the external validation cohort, the C-index is 0.712 (95% CI = 0.655-0.7692). Calibration curves demonstrated highly accurate predictions. When compared to the AJCC staging system, our model exhibited improved net benefits in DCA. Furthermore, the risk stratification system effectively differentiated patients with varying survival risks. We have created a novel nomogram for predicting the survival of Asian patients with SCLC. This nomogram has been subjected to external validation and has shown its superiority over the conventional TNM staging system. It offers a more precise and reliable means of forecasting the prognosis of Asian SCLC patients.

Comparison of proximal and distal gastric neuroendocrine carcinoma based on SEER database

The occurrence of gastric neuroendocrine carcinoma (GNEC) is on the rise, and its prognosis is extremely poor. We compared survival outcomes between distal and proximal GNEC and developed a nomogram incorporating tumor site to enhance personalized management for patients with GNEC. 1807 patients were divided into DGNEC and PGNEC groups. We performed analyses by using propensity score matching (PSM) and Fine-Gray competing risk methods. A predictive nomogram for the prognosis of GNEC was constructed and validated. The cumulative incidence of cancer-specific death (CSD) in the DGNEC group was lower than that in the PGNEC group. Subgroup analysis showed lower CSD of DGNEC in males, females, tumor sizes (≤ 2 cm, 2 < tumor size ≤ 5 cm, > 5 cm, and unknown), grade stage I-II, and AJCC stage I-III, chemotherapy or no chemotherapy, surgery or no surgery groups (P < 0.05). Multivariate analysis revealed a significant association between PGNEC and CSD (HR, 1.4; 95% CI 1.13–1.73; P = 0.02). The independent predictors of CSD in patients with GNEC were primary site, gender, age, tumor size, AJCC stage, T stage, N stage, grade stage, and surgery. A predictive model based on multivariate analysis was constructed to estimate the probability of CSD at 1-, 3-, and 5-year. The calibration curves demonstrated excellent consistency between the predicted and observed probabilities of CSD. Patients with DGNEC have a better prognosis than those with PGNEC. The model exhibits strong predictive capability for these patients.

Predicting the prognosis of patients with renal cell carcinoma based on the systemic immune inflammation index and prognostic nutritional index

The aim of the study was to analyze and discuss the value of preoperative systemic immune inflammation index (SII) and prognostic nutritional index (PNI) in predicting the prognosis of patients with renal cell carcinoma (RCC) after operation, and to establish a nomogram prediction model for patients with RCC after operation based on SII and PNI. From January 2014 to December 2018, 210 patients with RCC who underwent surgical treatment at the Xuzhou Central Hospital were selected as the research object. The receiver operating characteristic curve (ROC) was used to determine the optimal cut-off value for preoperative SII, PNI, LMR, PLR, NLR and the patients were divided into groups according to the optimal cutoff values. The survival rate of patients was evaluated. The risk factors that affect the prognosis of patients with RCC were determined by LASSO and Cox regression analysis, and a prognostic nomogram was constructed based on this result. The bootstrap method was used for internal verification of the nomogram model. The prediction efficiency and discrimination of the nomogram model were evaluated by the calibration curve and index of concordance (C-index), respectively. The average overall survival (OS) of all patients was 75.385 months, and the 1-, 2-and 3-year survival rates were 95.5%, 86.6% and 77.2%, respectively. The survival curve showed that the 5-year OS rate of low SII group was significantly higher than that of high SII group (89.0% vs. 64.5%; P < 0.05), and low PNI group was significantly lower than those in high PNI group (43.4% vs. 87.9%; p < 0.05). There were significant differences between preoperative SII and CRP, NLR, PLR, LMR, postoperative recurrence, pathological type and AJCC stage (P < 0.05). There were significant differences between preoperative PNI and BMI, platelet, NLR, PLR, LMR, postoperative recurrence, surgical mode and Fuhrman grade (P < 0.05). The ROC curve analysis showed that the AUC of PNI (AUC = 0.736) was higher than that of other inflammatory indicators, followed by the AUC of SII (0.718), and the difference in AUC area between groups was statistically significant (P < 0.05). The results from multivariate Cox regression analysis showed that SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade were independent risk factors for postoperative death of patients with RCC. According to the results of Cox regression analysis, a prediction model for the prognosis of RCC patients was established, and the C-index (0.918) showed that the model had good calibration and discrimination. The subject’s operating characteristic curve indicates that the nomogram has good prediction efficiency (the AUC = 0.953). Preoperative SII and PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade are closely related to the postoperative prognosis of patients with renal cell carcinoma. The nomogram model based on SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade has good accuracy, discrimination and clinical prediction efficiency.

Cell-cycle phase progression analysis identifies three unique phenotypes in soft tissue sarcoma

PurposeLoddo et al. (Br J Cancer 100:959–70, 2009) established the prognostic significance of cell cycle markers and "Cell-Cycle Phenotypes" in breast carcinoma. This study aims to 1) identify prognostic cell-cycle markers in sarcoma, and 2) assess the prognostic potential of specific cell-cycle phenotypes in sarcoma.MethodsTissue samples from 128 soft tissue sarcomas were stained for four cell cycle-specific markers: Mcm2, Geminin, Plk1, and H3S10ph. Only primary soft tissue tumors (liposarcoma, leiomyosarcoma, synovial sarcoma, and undifferentiated pleomorphic sarcoma) were included in the analysis. Any tumor coming from a recurrent or metastatic lesion were excluded from the analysis. Three cell-cycle phenotypes (I, II, III) were derived from marker expression patterns. Prognostic significance was evaluated in a subset of primary soft tissue sarcomas using Cox regression for survival analysis.ResultsCompared to phenotype I, the phenotype III tumors had a decreased 5-year overall survival (HR 6.81 [2.36–19.61]; p = < 0.001), 5-year disease-free survival (HR 1.07 (1.02–1.18); p = 0.004), and 5-year metastasis-free survival (HR 4.34 [1.58–11.93]; p = 0.004).High expression of Plk1 was associated with decreased 5-year overall survival (HR: 4.04 CI [1.21–6.67; p = 0.02) and 5-year metastasis-free survival (HR: 2.91 CI [1.15–7.37]; p = 0.03). Geminin was also found to have a decreased 5-year overall survival (HR:2.84 CI [1.21–6.67]; p = 0.02). No statistical difference in prognostication were noted between phenotypes and the AJCC system.ConclusionsWe identified three unique sarcoma cell cycle phenotypes that have prognostic significance. This performs similarly to the AJCC staging system.