Current Clinical Staging Research Articles

Risk Stratification in HPV-Associated Oropharyngeal Cancer: Limitations of Current Approaches and the Search for Better Solutions

The rising incidence of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) necessitates advancements in risk stratification to optimize treatment outcomes and improve the quality of life for patients. Despite its favorable prognosis compared to HPV-negative OPSCC, current clinical staging and biomarkers, such as p16 status, are limited in their ability to distinguish between high- and low-risk patients within HPV-associated OPSCC. This limitation results in the overtreatment of low-risk patients, exposing them to unnecessary toxicity, and the undertreatment of high-risk patients who require more aggressive interventions. This review critically evaluates current stratification methods, including clinical assessments, de-escalation trials, and candidate molecular biomarkers for risk stratification. Emerging approaches such as immune markers, viral genomic integration patterns, and other molecular markers offer promising avenues for enhanced prognostic accuracy. By integrating advanced risk stratification methods, tailored treatment approaches may one day be developed to balance oncologic efficacy with reduced treatment-related morbidity. This review underscores the need for continued research into predictive biomarkers and adaptive treatment strategies to better address the diverse risk profiles of HPV-associated OPSCC patients.

Are there features that can predict the unresectability of pleural mesothelioma?

The current clinical staging of pleural mesothelioma (PM) is often discordant with the pathologic staging. This study aimed to identify clinical and radiological features that could help predict unresectability in PM. Twenty-two descriptive radiologic features were retrospectively evaluated on preoperative computed tomography (CT) and/or positron emission tomography/CT (PET/CT) performed in patients with presumably resectable PM who underwent surgery. Measurements of maximum and sum pleural thickness at three levels of the thorax (upper, middle, and lower) were taken and stratified based on the cutpoints provided by the International Association for the Study of Lung Cancer (IASLC). Clinical and radiological features, including clinical-stage, were compared between resectable and unresectable tumors by univariate analysis and logistic regression modeling. Of 133 patients, 69/133 (52%) had resectable and 64/133 (48%) had unresectable PM. Asbestos exposure (p = 0.005), neoadjuvant treatment (p = 0.001), clinical T-stage (p < 0.0001), all pleural thickness measurements (p < 0.05), pleural thickness pattern (p < 0.0001) and degree (p = 0.033), lung invasion (p = 0.004), extrapleural space obliteration (p < 0.0001), extension to subphrenic space (p = 0.0004), and two combination variables representing extensive diaphragmatic contact and/or chest wall involvement (p = 0.002) and mediastinal invasion (p < 0.0001) were significant predictors at univariate analysis. At multivariable analysis, all models achieved a strong diagnostic performance (area under the curve (AUC) > 0.8). The two best-performing models were one that included the upper-level maximum pleural thickness, extrapleural space obliteration, and mediastinal infiltration (AUC = 0.876), and another that integrated clinical variables and radiological assessment through the clinical T-stage (AUC = 0.879). Selected clinical and radiologic features, including pleural thickness measurements, appear to be strong predictors of unresectability in PM. A more accurate prediction of unresectability in the preoperative assessment of patients with pleural mesothelioma may avoid unnecessary surgery and prompt initiation of nonsurgical treatments. About half of pleural mesothelioma patients are reported to receive an incorrect disease stagepreoperatively. Eleven features identified as predictors of unresectability were included in strongly performing predictive models. More accurate preoperative staging will help clinicians and patients choose the most appropriate treatments.

Preoperative and postoperative MRI-based models versus clinical staging systems for predicting early recurrence in hepatocellular carcinoma

BackgroundTo predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems. MethodsOne hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems. ResultsSix variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models. ConclusionThe preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.

Prognostic value of CT-based radiomics in grade 1–2 pancreatic neuroendocrine tumors

BackgroundSurgically resected grade 1–2 (G1-2) pancreatic neuroendocrine tumors (PanNETs) exhibit diverse clinical outcomes, highlighting the need for reliable prognostic biomarkers. Our study aimed to develop and validate CT-based radiomics model for predicting postsurgical outcome in patients with G1-2 PanNETs, and to compare its performance with the current clinical staging system.MethodsThis multicenter retrospective study included patients who underwent dynamic CT and subsequent curative resection for G1–2 PanNETs. A radiomics-based model (R-score) for predicting recurrence-free survival (RFS) was developed from a development set (441 patients from one institution) using least absolute shrinkage and selection operator-Cox regression analysis. A clinical model (C-model) consisting of age and tumor stage according to the 8th American Joint Committee on Cancer staging system was built, and an integrative model combining the C-model and the R-score (CR-model) was developed using multivariable Cox regression analysis. Using an external test set (159 patients from another institution), the models’ performance for predicting RFS and overall survival (OS) was evaluated using Harrell’s C-index. The incremental value of adding the R-score to the C-model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe median follow-up periods were 68.3 and 59.7 months in the development and test sets, respectively. In the development set, 58 patients (13.2%) experienced recurrence and 35 (7.9%) died. In the test set, tumors recurred in 14 patients (8.8%) and 12 (7.5%) died. In the test set, the R-score had a C-index of 0.716 for RFS and 0.674 for OS. Compared with the C-model, the CR-model showed higher C-index (RFS, 0.734 vs. 0.662, p = 0.012; OS, 0.781 vs. 0.675, p = 0.043). CR-model also showed improved classification (NRI, 0.330, p < 0.001) and discrimination (IDI, 0.071, p < 0.001) for prediction of 3-year RFS.ConclusionsOur CR-model outperformed the current clinical staging system in prediction of the prognosis for G1–2 PanNETs and added incremental value for predicting postoperative recurrence. The CR-model enables precise identification of high-risk patients, guiding personalized treatment planning to improve outcomes in surgically resected grade 1–2 PanNETs.

Personalised decision making to predict absolute metastatic risk in cutaneous squamous cell carcinoma: development and validation of a clinico-pathological model

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer, affecting more than 2 million people worldwide yearly and metastasising in 2-5% of patients. However, current clinical staging systems do not provide estimates of absolute metastatic risk, hence missing the opportunity for more personalised treatment advice. We aimed to develop a clinico-pathological model that predicts the probability of metastasis in patients with cSCC. Nationwide cohorts from (1) all patients with a first primary cSCC in The Netherlands in 2007-2008 and (2) all patients with a cSCC in 2013-2015 in England were used to derive nested case-control cohorts. Pathology records of primary cSCCs that originated a loco-regional or distant metastasis were identified, and these cSCCs were matched to primary cSCCs of controls without metastasis (1:1 ratio). The model was developed on the Dutch cohort (n=390) using a weighted Cox regression model with backward selection and validated on the English cohort (n=696). Model performance was assessed using weighted versions of the C-index, calibration metrics, and decision curve analysis; and compared to the Brigham and Women's Hospital (BWH) and the American Joint Committee on Cancer (AJCC) staging systems. Members of the multidisciplinary Skin Cancer Outcomes (SCOUT) consortium were surveyed to interpret metastatic risk cutoffs in a clinical context. Eight out of eleven clinico-pathological variables were selected. The model showed good discriminative ability, with an optimism-corrected C-index of 0.80 (95% Confidence interval (CI) 0.75-0.85) in the development cohort and a C-index of 0.84 (95% CI 0.81-0.87) in the validation cohort. Model predictions were well-calibrated: the calibration slope was 0.96 (95% CI 0.76-1.16) in the validation cohort. Decision curve analysis showed improved net benefit compared to current staging systems, particularly for thresholds relevant for decisions on follow-up and adjuvant treatment. The model is available as an online web-based calculator (https://emc-dermatology.shinyapps.io/cscc-abs-met-risk/). This validated model assigns personalised metastatic risk predictions to patients with cSCC, using routinely reported histological and patient-specific risk factors. The model can empower clinicians and healthcare systems in identifying patients with high-risk cSCC and offering personalised care/treatment and follow-up. Use of the model for clinical decision-making in different patient populations must be further investigated. PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.

Distinct subtypes of endometriosis identified based on stromal-immune microenvironment and gene expression: implications for hormone therapy.

Endometriosis (EMs) is a chronic inflammatory condition that is highly heterogeneous. Current clinical staging fails to accurately predict drug responses and prognosis. In this study, we aimed to reveal the heterogeneity of ectopic lesions and investigate the possible underlying mechanisms using transcriptomic data and clinical information. The EMs microarray dataset GSE141549 was obtained from the Gene Expression Omnibus database. Unsupervised hierarchical clustering was performed to identify EMs subtypes, which was followed by the functional enrichment analysis and estimation of immune infiltrates. Subtype-associated gene signatures were identified and further validated in other independent datasets, including GSE25628, E-MTAB-694, and GSE23339. Additionally, tissue microarrays (TMAs) were generated from premenopausal patients with EMs to investigate the potential clinical implications of the two identified subtypes. The unsupervised clustering analysis revealed that ectopic EMs lesions can be classified into two distinct subtypes: stroma-enriched (S1) and immune-enriched (S2). The functional analysis revealed that S1 correlated with fibroblast activation and extracellular matrix remodeling in the ectopic milieu, whereas S2 was characterized by the upregulation of immune pathways and a higher positive correlation with the immunotherapy response. Moreover, we identified a subtype signature composed of FHL1 and SORBS1, and constructed a subtype diagnostic model. Based on the cohort data from the TMAs, we found that S2 was strongly associated with the failure of/intolerance to hormone therapy. This study identified two distinct subtypes that are varyingly associated with hormone resistance, stroma-immunity, and molecular features, thereby highlighting the importance of this stromal-immune heterogeneity in identifying EMs subtypes and providing novel insights into future personalized hormone-free therapy in EMs.

Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning.

Develop a prediction model to determine the probability of no lymph node metastasis (pN0) in patients with colorectal cancer. We used data from four Danish health databases on patients with colorectal cancer diagnosed between 2001 and 2019. The registries were harmonized into one common data model (CDM). Patients with clinical T4 tumors, undergoing palliative or acute surgery, and patients undergoing neoadjuvant therapy were excluded. Preoperative data was used to train the model. A postoperative model including tumor-specific variables potentially available after local tumor resection was also developed. Additionally, both models were compared with a model based on age, sex, and clinical N stage to resemble current standards. A Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis for prediction was used. In total, 35,812 patients with 16,802 variables were identified in the CDM, and 194 variables affected the probability of pN0 preoperative. The area under the receiver operating characteristic curve (AUROC) was 0.64 (95% CI 0.63-0.66), and the area under the precision-recall curve (AUPRC) was 0.75 (95% CI 0.74-0.76). The mean predicted risk was 0.649, observed risk was 0.650, and calibration-in-large was 0.998. Adding histopathological data from the tumor improved the model slightly by increasing AUROC to 0.69. In comparison, the AUROC of the current standard clinical staging model was 0.57. Using Danish National Patient Registry data in a machine learning-based predictive model showed acceptable results and outperforms current tools for clinical staging in predicting pN0 status in patients scheduled for CRC surgery.

Clinical models to predict lymph nodes metastasis and distant metastasis in newly diagnosed early esophageal cancer patients: A population-based study.

Patients with early esophageal cancer (EC) receive individualized therapy based on their lymph node metastasis (LNM) and distant metastasis (DM) status; however, deficiencies in current clinical staging techniques and the issue of cost-effectiveness mean LNM and DM often go undetected preoperatively. We aimed to develop three clinical models to predict the likelihood of LNM, DM, and prognosis in patients with early EC. The Surveillance, Epidemiology, and End Results database was queried for T1 EC patients from 2004 to 2015. Multivariable logistic regression and Cox proportional hazards models were used to recognize the risk factors of LNM and DM, predict overall survival (OS), and develop relevant nomograms. Receiver operating characteristic (ROC)/concordance index and calibration curves were used to evaluate the discrimination and accuracy of the three nomograms. Decision curve analyses (DCAs), clinical impact curves, and subgroups based on model scores were used to determine clinical practicability. The area under the curve of the LNM and DM nomograms were 0.668 and 0.807, respectively. The corresponding C-index of OS nomogram was 0.752. Calibration curves and DCA showed an effective predictive accuracy and clinical applicability. In patients with T1N0M0 EC, surgery alone (p < 0.01) proved a survival advantage. Chemotherapy and radiotherapy indicated a better prognosis in the subgroup analysis for T1 EC patients with LNM or DM. We created three nomograms to predict the likelihood of LNM, DM, and OS probability in patients with early EC using a generalizable dataset. These useful visual tools could help clinical physicians deliver appropriate perioperative care.

201. PREDICTION OF LYMPH NODE METASTASIS IN PATIENTS WITH ESOPHAGEAL SQUAMOUS CELL CARCINOMA USING GENETIC SIGNATURES FROM LARGE-SCALE GENOMIC REPERTOIRES

Abstract The presence of lymph node metastasis puts patients with resectable esophageal squamous cell carcinoma (ESCC) at a high risk of disease recurrence, and these patients would benefit from neoadjuvant treatment. However, current clinical staging is not enough to capture all patients with high-risk disease, and novel diagnostic tools are required to improve the accuracy of treatment planning. We tried to explore a better prediction using genomic features from large-scale gene mutation data. The major public data repositories of whole-exome sequencing for ESCC were involved in this study. The absolute differences in gene mutation frequency were calculated using Fisher’s exact test in nodal negative versus nodal positive patients. The classification of gene mutation status was dichotomized as wild-type and mutant type. To find a reliable gene panel that could provide sufficient population coverage, we set the cut-off level above 85% in both nodal phenotypes. The genomic classifier for nodal metastasis risk prediction was further generated by LASSO logistic regression analysis with 10-fold cross-validation based on the topmost significantly different genes. Above the threshold values, a total of 120 differentially mutated genes were identified in the 561 ESCC genomic sequencing data. The population coverage rates were 85.97% in the nodal positive group (n = 335) and 88.05% in the nodal negative group (n = 226), respectively. According to the LASSO logistic regression approach, a panel of 112 topmost relevant genes (termed LNM112) was selected. The receiver operating characteristic curve of the LNM112 predictive model was plotted with an area under the curve value of 0.95 (95% CI: 0.93–0.96). The sensitivity and specificity of the LNM112 model were 96.72% (324/335) and 71.24% (161/226), respectively. We identified a genomic signature (112 genes) from the large-scale genomic repertoires capable of predicting lymph node metastasis in ESCC. It can be incorporated into the current staging modalities to help inform treatment decisions, and external validation is warranted in different populations.

A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients.

BackgroundClinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).MethodsA total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.ResultsA total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.ConclusionThe established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

Application of vestibular function examination battery in the staging of vestibular function in Meniere's disease

Objective:To investigate the staging of vestibular organ damage in Meniere's disease, based on the vestibular function examination battery. Methods:Thirty-nine patients, clinically diagnosed as unilateral Meniere's disease, underwent audiologic test and vestibular function examination battery, including ocular vestibular evoked myogenic potential（oVEMP）, cervical vestibular evoke myogenic potential（cVEMP）, and caloric tests. Based on the results of the vestibular function examination battery, the vestibular function was divided into 4 stages. StageⅠ: oVEMP, cVEMP, and caloric tests were normal; stage Ⅱ: any one test of the three examinations was abnormal; stage Ⅲ: two of the three examinations were abnormal; Stage Ⅳ: All the three examinations were abnormal. Results:According to the vestibular function staging strategy in this study, patients in stage Ⅰ, Ⅱ, Ⅲ, Ⅳ were 7.7%（3/39）, 30.8%（12/39）, 33.3%（13/39）, 28.2%（11/39） respectively in the 39 Meniere's disease patients. However, according to the current clinical staging strategy of Meniere's disease, patients of stage1, 2, 3, 4 were 20.5%（8/39）, 43.6%（17/39）, 28.2%（11/39）, and 7.7%（3/39） respectively. 37.5%（3/8） patients in stage 1 and 64.7%（11/17） patients in stage 2 had two or more abnormal vestibular organs. While all the patients in stage 4 had abnormal semicircular canals, utricle, and saccule. The stage of vestibular function was correlated with the distribution of current clinical staging strategy of Meniere's disease（P<0.05）. Conclusion:The combination of oVEMP, cVEMP and caloric tests can divide the vestibular function into four stages, which can be used as a supplement to the traditional vestibular evaluation and clinical staging based on audiology in Meniere's disease.

Integrative multi-omics profiling of resectable pancreatic cancer reveals clinically relevant molecular subtypes with precision strategies beyond the clinical staging system

Even though the current clinical staging system for resectable pancreatic cancer has validated major clinicopathologic factors in multiple clinical cohorts, there is still an unmet need for integrative consideration using multi-omics data to stratify the patients with pancreatic cancer elaborately. We performed a comprehensive analysis and profiling using genomic, transcriptomic, and proteomic data from TCGA-PAAD (n = 150) and other translational cohorts (4 cohorts, n = 340). Molecular features and major subtypes were analyzed mutually with clinical and pathologic factors to discover a clinically relevant translational staging system. The correlation analysis for each 50 cancer-specific molecular pathways with the clinical staging system revealed no remarkable pattern of the association. The mutational pattern of KRAS and several transcriptomic pathways, such as epithelial-mesenchymal transition and DNA repair, were differently presented in each clinical stage from the 8th AJCC TNM staging system. From the cluster of cluster analysis, we identified three consensus molecular subtypes, specifically subtype A was remarkably correlated with previously known aggressive subtypes, Basal, Mesenchymal, and Squamous subtypes, and subtype B and C were correlated with other subtypes regarding classical, exocrine, immunogenic, progenitor and ADEX subtypes. This consensus subtypes were validated at the independent pancreatic cancer genomics cohorts. Our in-silico prediction revealed subtype-specific biomarkers and potential therapeutics, including molecular target agents as well as immunotherapeutic options. Our comprehensive multi-omics analysis reveals prognostically significant consensus subtypes showing distinct tumor biology with unique therapeutic opportunities including biomarkers with high performance. This study provides exact needs for clinical trials based on translational approaches to establish precision surgical oncology.

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.

Accuracy of clinical staging for T2N0 oesophageal cancer: systematic review and meta-analysis.

Oesophageal cancer is the sixth commonest cause of overall cancer mortality. Clinical staging utilizes multiple imaging modalities to guide treatment and prognostication. T2N0 oesophageal cancer is a treatment threshold for neoadjuvant therapy. Data on accuracy of current clinical staging tests for this disease subgroup are conflicting. We performed a meta-analysis of all primary studies comparing clinical staging accuracy using multiple imaging modalities (index test) to histopathological staging following oesophagectomy (reference standard) in T2N0 oesophageal cancer. Patients that underwent neoadjuvant therapy were excluded. Electronic databases (MEDLINE, Embase, Cochrane Library) were searched up to September 2019. The primary outcome was diagnostic accuracy of combined T&N clinical staging. Publication date, first recruitment date, number of centers, sample size and geographical location main histological subtype were evaluated as potential sources of heterogeneity. The search strategy identified 1,199 studies. Twenty studies containing 5,213 patients met the inclusion criteria. Combined T&N staging accuracy was 19% (95% CI, 15-24); T staging accuracy was 29% (95% CI, 24-35); percentage of patients with T downstaging was 41% (95% CI, 33-50); percentage of patients with T upstaging was 28% (95% CI, 24-32) and percentage of patients with N upstaging was 34% (95% CI, 30-39). Significant sources of heterogeneity included the number of centers, sample size and study region. T2N0 oesophageal cancer staging remains inaccurate. A significant proportion of patients were downstaged (could have received endotherapy) or upstaged (should have received neoadjuvant chemotherapy). These findings were largely unchanged over the past two decades highlighting an urgent need for more accurate staging tests for this subgroup of patients.

Optimal Size Criterion for Malignant Lymph Nodes and a Novel Lymph Node Clinical Staging System for Unresectable Esophageal Squamous Cell Carcinoma: Evaluation by Multislice Spiral Computed Tomography.

Objectives: The current Chinese draft nodal clinical staging system for unresectable esophageal cancer is controversial. Our study aimed to propose a new diagnostic criterion for lymph node metastasis (LNM) detected by multislice spiral computed tomography (MSCT) in nonsurgically treated esophageal squamous cell carcinoma (ESCC) patients and then develop a novel lymph node (LN) clinical staging system for better individual prognostic prediction.Methods: The short-axis diameters of regional LNs were measured in 393 nonsurgical patients. Regional nodes were considered positive for malignancy if the nodal size exceeded the optimal size, which was determined by Kaplan-Meier survival analysis. The novel LN clinical staging system was then constructed using the LASSO model based on the relative prognostic importance of different LN stations. Validation cohort was included to confirm the prognostic performance.Results: Regional nodes were considered positive for malignancy if they were larger than 10 mm in the low cervical and upper thoracic segments, 7 mm in the middle thoracic segment, and 8 mm in the lower thoracic and celiac segments. Using the LASSO model, stations 2R, 3A, 7 and 16 were qualified in the model. Further analysis showed that our LN clinical staging system had better homogeneity, discriminatory ability and clinical value than the draft nodal staging system.Conclusions: Our results show that the new diagnostic criterion may improve the diagnostic value of MSCT in metastatic LNs. The novel LN clinical staging system can stratify nonsurgically treated ESCC patients into different risk groups, providing valuable information for decision making and outcome prediction.

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram

IntroductionSurvival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC). Materials and methodsOn the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores. ResultsOn multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories. ConclusionsThe risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.

MRI-Diagnosed Tumor Deposits and EMVI Status Have Superior Prognostic Accuracy to Current Clinical TNM Staging in Rectal Cancer.

MRI assessment of rectal cancer not only assesses tumor depth and surgical resectability but also extramural disease which affects prognosis. We have observed that nonnodal tumor nodules (tumor deposits; mrTDs) have a distinct MRI appearance compared to lymph node metastases (mrLNMs). We aimed to assess whether mrTDs and mrLNMs have different prognostic implications and compare these to other known prognostic markers. This was a retrospective cohort study of 233 patients undergoing resection for rectal cancer from January 2007 to October 2015. Data were obtained from electronic records and MRIs blindly rereported. Survival was determined using Kaplan-Meier method. Prognostic markers were evaluated using Cox regression and competing risks analysis. Inter-observer agreement for mrTD was measured using Cohen Kappa. On multivariable analysis, baseline mrTD/mrEMVI (extramural venous invasion) status was the only significant MRI factor for adverse survival [hazard ratio (HR) 2.36 (1.54-3.61] for overall survival, 2.37 (1.47-3.80) for disease-free survival (both P < 0.001), superseding T and N categories. mrLNMs were associated with good prognosis (HR 0.50 (0.31-0.80) P = 0.004 for overall survival, 0.60 (0.40-0.90) P = 0.014 for disease-free survival). On multivariable analysis, mrTDs/mrEMVI were strongly associated with distant recurrence (HR 6.53 (2.52-16.91) P ≤ 0.001) whereas T and N category were not. In a subgroup analysis of posttreatment MRIs in postchemoradiotherapy patients, mrTD/mrEMVI status was again the only significant prognostic factor; furthermore those who showed a good treatment response had a prognosis similar to patients who were negative at baseline. Inter-observer agreement for detection of mrTDs was k0.77 and k0.83. Current MRI staging predicting T and N status does not adequately predict prognosis. Positive mrTD/mrEMVI status has greater prognostic accuracy and would be superior in determining treatment and follow-up protocols. Chemoradiotherapy may be a highly effective treatment strategy in mrTD/mrEMVI positive patients.

Diabetic Kidney Disease in Childhood and Adolescence: Conventional and Novel Renoprotective Strategies

Diabetic kidney disease (DKD) is defined as a clinical syndrome consisting of persistent macroalbuminuria, progressive decline in glomerular filtration rate (GFR), hypertension, increased cardiovascular disease events, and the associated mortality of these conditions. The disease evolves from the microvascular complications of poorly controlled Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM). The pathogenic pathways comprise renal haemodynamic changes, ischaemia and inflammation, and overactive renin–angiotensin–aldosterone system (RAAS), through which several events cascade down from hyperglycaemia to renal fibrosis. Conventional and novel renoprotective strategies target modifiable DKD risk factors and specific stages of the pathogenic pathways, respectively. Although these strategies may slow DKD progression to end-stage kidney disease (ESKD), novel drugs are still undergoing trials for validation in human participants. This narrative review appraises these renoprotective strategies and highlights the current clinical staging and pathogenesis of the disease.

Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.

We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (P=0.008) for OS, 0.752 (P=0.053) for DMFS, and 0.721 (P=0.025) for LRFS. The Kaplan-Meier (KM) test values for II/I, III/II, IV/III groups in our new machine learning-based scoring system were 0.011, 0.010, and <0.001, respectively, whereas those for II/I, III/II, IV/III groups in the TNM/American Joint Committee on Cancer (AJCC) system were 0.118, 0.121, and <0.001, respectively. Significant differences were observed in the new machine learning-based scoring system analysis of each curve (P < 0.05), whereas the P values of curves obtained from the TNM/AJCC system, between II/I and III/II, were 0.118 and 0.121, respectively, without a significant difference. In conclusion, the AutoML algorithm demonstrated better prognostic performance than the TNM/AJCC system for NPC. The algorithm showed a good potential for clinical application and may aid in improving counseling and facilitate the personalized management of patients with NPC. The clinical application of our new scoring and staging system may significantly improve precision medicine.