Current TNM Staging System Research Articles

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer

The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

Prognostic impact of muscle invasion in buccal mucosa squamous cell carcinoma.

The objective of the study was to assess the prognostic value of muscle invasion (MI), a key histopathological feature of tumor aggressiveness, and construct a superior prognostic prediction model combining the current TNM staging system. MI was analyzed in the whole-slide images from a total of 301 patients with primary buccal mucosa squamous cell carcinoma (BMSCC). Survival times of patients with/without MI were evaluated by Kaplan-Meier analysis. MI was further combined with the TNM staging system to explore its predictive value for prognosis. Moreover, 204 cases of head and neck carcinoma from the TCGA database were included. MI positive rate reached to 76% (229/301) in patients with BMSCC. MI was associated with poor overall survival (p = 0.012) and disease-free survival (p = 0.022). The novel system (TNM staging combined with MI) revealed strong predictive performance, with the largest area under the curve (OS: p < 0.001, DFS: p < 0.004). MI and the established classification system were also had good predictive ability in the TCGA cohort. MI is an independent predictor of poor prognosis of BMSCC. The inclusion of MI in prediction system can augment the risk stratification of patients with oral squamous cell carcinoma and may assist in the clinical decision-making process.

A new staging system for postoperative prognostication in pancreatic ductal adenocarcinoma

The current TNM staging system for pancreatic ductal adenocarcinoma (PDAC) has revised the definitions of T and N categories as well as stage groups. However, studies validating these modifications have yielded inconsistent results. The existing TNM staging system in prognostic prediction remains unsatisfactory. The prognosis of PDAC is closely associated with pathological and biological factors. Herein, we propose a new staging system incorporating distant metastasis, postoperative serum levels of CA19-9 and CEA, tumor size, lymph node metastasis, lymphovascular involvement, and perineural invasion to enhance the accuracy of prognosis assessment. The proposed staging system exhibited a strong correlation with both overall survival and recurrence-free survival, effectively stratifying survival into five distinct tiers. Additionally, it had favorable discrimination and calibration. Thus, the proposed staging system demonstrates superior prognostic performance compared to the TNM staging system, and can serve as a valuable complementary tool to address the limitations of TNM staging in prognostication.

The ability of the eighth edition of the TNM staging system plus minor invasion criteria to predict the biological behaviour of oral cavity carcinomas.

Oral squamous cell carcinoma (OSCC) treatment is based largely on the TNM stage. The eighth edition includes important new prognostic parameters (extranodal extension and depth of invasion), while it does not consider tumour molecular characteristics or minor invasion criteria (perineural and lymphovascular invasion, grading and resection margins). This study evaluated how well the TNM eighth edition predicts the biological behaviour of OSCC, considering survival and risk of locoregional recurrence. Data from 217 patients treated for OSCC were analysed, including epidemiologic characteristics, histological features and treatment. No significant correlations with overall survival or tumour recurrence were found for pT stages and the type of treatment, while different pN stages had significant differences in recurrence, but not in overall survival. We found significant correlations between overall survival and tumour grade and lymphovascular and perineural invasion and a significant correlation between tumour resection margins and the risk of recurrence. The current TNM staging system is a necessary but not sufficient tool for predicting the overall survival and risk of recurrence of OSCC. It could be improved by considering other factors, such as minor invasion criteria and biological markers.

Identification and initial validation of maximal tumor area as a novel prognostic factor for overall and disease-free survival in patients with resectable colon cancer: a retrospective study.

The tumor area may be a potential prognostic indicator. The present study aimed to determine and validate the prognostic value of tumor area in curable colon cancer. This retrospective study included a training and validation cohorts of patients who underwent radical surgery for colon cancer. Independent prognostic factors for overall survival (OS) and disease-free survival (DFS) were identified using Cox proportional hazards regression models. The prognostic discrimination was evaluated using the integrated area under the receiver operating characteristic curves (iAUCs) for prognostic factors and models. The prognostic discrimination between tumor area and other individual factors was compared, along with the prognostic discrimination between the tumor-node-metastasis (TNM) staging system and other prognostic models. Two-sample Wilcoxon tests were carried out to identify significant differences between the two iAUCs. A two-sided P <0.05 was considered statistically significant. A total of 3051 colon cancer patients were included in the training cohort and 872 patients in the validation cohort. Tumor area, age, differentiation, T stage, and N stage were independent prognostic factors for both OS and DFS in the training cohort. Tumor area had a better OS and DFS prognostic discrimination characteristics than T stage, maximal tumor diameter, differentiation, tumor location, and number of retrieved lymph nodes. The novel prognostic model of T stage + N stage + tumor area (iAUC for OS, 0.714, P <0.001; iAUC for DFS, 0.694, P <0.001) showed a better prognostic discrimination than the TNM staging system (T stage + N stage; iAUC for OS, 0.664; iAUC for DFS, 0.658). Similar results were observed in an independent validation cohort. Tumor area was identified as an independent prognostic factor for both OS and DFS in curable colon cancer patients, and in cases with an adequate number of retrieved lymph nodes. The novel prognostic model of combining T stage, N stage, and tumor area may be an alternative to the current TNM staging system.

Epidemiology, staging and management of mucosal melanoma of the head and neck: a narrative review.

Mucosal melanoma of the head and neck (MMHN) are rare, aggressive neoplasms of melanocyte origin that remain incompletely understood and have a poor prognosis, with high rates of locoregional recurrence and distant metastasis. Several recent studies having expanded understanding of MMHN, we undertook a review of the latest evidence pertaining to its epidemiology, staging, and management. A literature search was conducted for peer-reviewed articles reporting and discussing the epidemiology, staging, and management of MMHN. PubMed, Medline, Embase and the Cochrane Library were searched to identify relevant publications. MMHN remains an uncommon disease. The current TNM staging system for MMHN provides inadequate risk stratification, and consideration of an alternative staging model such as one based on a nomogram may be justifiable. Tumour resection with clear histological margins remains the cornerstone of optimal treatment. Adjuvant radiotherapy may improve locoregional control but does not appear to affect survival. Immune checkpoint inhibitors and c-KIT inhibitors demonstrate promising efficacy in patients with advanced or unresectable mucosal melanomas, and warrant further research exploring the utility of combination therapies. Their roles as adjuvant therapies have not been determined. The efficacy of neoadjuvant systemic therapy is also not yet clear, although early results suggest that it may improve outcomes. New insights into the epidemiology, staging and management of MMHN have transformed the standard of care for this rare malignancy. Nonetheless, the results of ongoing clinical trials and future prospective studies are required to better understand this aggressive disease and optimise its management.

Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer.

Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematically evaluated the role of lipid droplet metabolism-related genes (LDMRGs) in patients with gastric cancer. We identified two distinct molecular subtypes in the TCGA-STAD cohort based on LDMRGs expression. We then constructed risk prediction scoring models in the TCGA-STAD cohort by lasso regression analysis and validated the model with the GSE15459 and GSE66229 cohorts. Moreover, we constructed a nomogram prediction model by cox regression analysis and evaluated the predictive efficacy of the model by various methods in STAD. Finally, we identified the key gene in LDMRGs, ABCA1, and performed a systematic multi-omics analysis in gastric cancer. Two molecular subtypes were identified based on LDMRGs expression with different survival prognosis and immune infiltration levels. lasso regression models were effective in predicting overall survival (OS) of gastric cancer patients at 1, 3 and 5 years and were validated in the GEO database with consistent results. The nomogram prediction model incorporated additional clinical factors and prognostic molecules to improve the prognostic predictive value of the current TNM staging system. ABCA1 was identified as a key gene in LDMRGs and multi-omics analysis showed a strong correlation between ABCA1 and the prognosis and immune status of patients with gastric cancer. This study reveals the characteristics and possible underlying mechanisms of LDMRGs in gastric cancer, contributing to the identification of new prognostic biomarkers and providing a basis for future research.

Establishment of a prognostic nomogram for patients with locoregionally advanced nasopharyngeal carcinoma incorporating clinical characteristics and dynamic changes in hematological and inflammatory markers.

This study aims to investigate the prognostic value of changes in hematological and inflammatory markers during induction chemotherapy (IC) and concurrent chemo-radiation (CCRT), thus construct nomograms to predict progression free survival (PFS) of patients with locally advanced nasopharyngeal carcinoma (LANPC). 130 patients were included in this prospective analysis. Univariate and multivariate cox regression analyses were conducted to identify prognostic factors. Three multivariate analyses integrating different groups of variables were conducted independently. Concordance indexes (c-index), calibration plots and Kaplan-Meier curves were used to evaluate the nomograms. Bootstrap validation was performed to determine the accuracy of the nomogram using 1000 resamples. The performances of proposed nomograms and TNM staging system were compared to validate the prognostic value of hematological and inflammatory markers. Pretreatment gross tumor volume of nodal disease (GTVn), Δe/bHGB (hemoglobin count at end of treatment/baseline hemoglobin count), and stage were selected as predictors for 3-year PFS in first multivariate analysis of clinical factors. The second multivariate analysis of clinical factors and all hematological variables demonstrated that ΔminLYM (minimum lymphocyte count during CCRT/lymphocyte count post-IC), pretreatment GTVn and stage were associated with 3-year PFS. Final multivariate analysis, incorporating all clinical factors, hematological variables and inflammatory markers, identified the following prognostic factors: pretreatment GTVn, stage, ΔmaxPLR (maximum platelet-to-lymphocyte ratio (PLR) during CCRT/PLR post-IC), and ΔminPLT (minimum platelet count during CCRT/platelet count post-IC). Calibration plots showed agreement between the PFS predicted by the nomograms and actual PFS. Kaplan-Meier curves demonstrated that patients in the high-risk group had shorter PFS than those in the low-risk group (P ≤ 0.001). The c-indexes of the three nomograms for PFS were 0.742 (95% CI, 0.639-0.846), 0.766 (95% CI, 0.661-0.871) and 0.815 (95% CI,0.737-0.893) respectively, while c-index of current TNM staging system was 0.633 (95% CI, 0.531-0.736). We developed and validated a nomogram for predicting PFS in patients with LANPC who received induction chemotherapy and concurrent chemo-radiation. Our study confirmed the prognostic value of dynamic changes in hematological and inflammatory markers. The proposed nomogram outperformed the current TNM staging system in predicting PFS, facilitating risk stratification and guiding individualized treatment plans.

A modified mTNM staging system based on lymph node ratio for colon neuroendocrine tumors: A recursive partitioning analysis.

In the current tumor-lymph node-metastasis (TNM) staging system for colon neuroendocrine tumors, lymph node status is divided into N1 and N0. An assessment of the lymph node ratio (LNR) and a proposal for a modified mTNM staging system were the objectives of this study. Selecting the optimal cut-off value of LNR was done using X-tile. A Cox regression model and the Kaplan-Meier method were performed to calculate patient cancer-specific survival in the Surveillance, Epidemiology and End Results cohort. Recursive partitioning analysis was used to improve TNM staging. The study included 674 patients. The current TNM staging system showed inadequate discriminatory power between stage I and stage II patients (p = 0.088). The optimal cut-off value was determined as 0.6 for LNR. Based on multivariate Cox regression analysis, the modified mN classification could be classified into mN 0 (LNR = 0.00), mN 1 (LNR = 0.01-0.60), and mN 2 (LNR > 0.60), and was found to be an independent factor affecting prognosis (p < 0.001). Using the American Joint Committee on Cancer T and modified mN classifications, the modified mTNM system was constructed, and it exhibited better prognostic discriminatory power ability than the traditional TNM system (C-index: 0.587 vs. 0.665). Our study determined that LNR is a prognostic factor in colon NET patients. In addition, to more accurately assess the prognosis of colon NET patients, we proposed a modified mTNM staging system.

A critical analysis of the current TNM classification for differentiated thyroid carcinoma in young patients: Time for a change?

The current TNM classification that simply classifies differentiated thyroid carcinoma (DTC) patients younger than 55 years into stage I and stage II based on the presence or absence of distant metastases has been questioned. In this study, we reexamined the impact of T status and N status on prognosis and then developed a new prediction model to improve the predictability of cancer-specific survival (CSS) in young patients. Kaplan-Meier method was applied to calculate the CSS. Multivariable Cox proportional hazards models were used to assess the impact of T status and N status on CSS after adjustment for known covariates. The area under the receiver operating characteristic curve (AUC), C-index, Bayesian information criterion (BIC), and Akaike information criterion (AIC) were calculated to compare model performance. A total of 9,242 DTC patients younger than 55 years were enrolled in the study. After adjusting for gender, age at diagnosis, race, pathology subtype, N stage, and M stage, T3 disease [hazard ratio (HR): 3.78, P = 0.006] and T4 disease (HR: 7.96, P < 0.001) remain independent predictors of CSS. Similarly, the 10-year CSS rate of N1b disease (HR: 3.78, P < 0.001) was significantly higher than that of N0 disease after adjustment. Moreover, Kaplan-Meier survival analysis showed that the 10-year CSS of stage II disease in younger patients with DTC showed a sharp decrease compared with that in older patients with DTC (74.47% vs. 98.43%, P < 0.001). Furthermore, a modified TNM staging system based on significantly prognostic T stage and N stage was established, which showed better performance than the current TNM staging system (P < 0.05). The new prediction model is also applicable to papillary thyroid carcinoma patients and follicular thyroid carcinoma patients. This is the first study to question the rationality of the current TNM staging system for patients younger than 55 years and successfully develop a new prognostic model, which improves prognostic stratification and guides individualized management.

Establishment of a circular RNA regulatory stemness-related gene pair signature for predicting prognosis and therapeutic response in colorectal cancer.

BackgroundColorectal cancer (CRC) is a common malignant tumor of the digestive tract with a poor prognosis. Cancer stem cells (CSCs) affect disease outcomes and treatment responses in CRC. We developed a circular RNA (circRNA) regulatory stemness-related gene pair (CRSRGP) signature to predict CRC patient prognosis and treatment effects.MethodsThe circRNA, miRNA, and mRNA expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CRSRGPs were established based on stemness-related genes in the competing endogenous RNA (ceRNA) network. A CRSRGP signature was generated using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis of TCGA training set. The prognosis was predicted by generating a nomogram integrating the CRSRGP signature and clinicopathologic features. The model was validated in an external validation set (GSE17536). The antitumor drug sensitivity and immunotherapy responses of CRC patients in the high-risk group (HRG) and low-risk group (LRG) were evaluated by the pRRophetic algorithm and immune checkpoint analysis.ResultsWe established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients. In the training and external validation sets, risk scores were used to categorize CRC patients into the HRG and LRG. The Kaplan–Meier analysis showed a poor prognosis for patients in the HRG and that subgroups with different clinical characteristics had significantly different prognoses. A multivariate Cox analysis revealed that the CRSRGP signature was an independent prognostic factor. The nomogram integrating clinical features and the CRSRGP signature efficiently predicted CRC patient prognosis, outperformed the current TNM staging system, and had improved practical clinical value. Anticancer drug sensitivity predictions revealed that the tumors of patients in the HRG were more sensitive to pazopanib, sunitinib, gemcitabine, lapatinib, and cyclopamine. Analysis of immune checkpoint markers demonstrated that patients in the HRG were more likely to benefit from immunotherapy.ConclusionAn efficient, reliable tool for evaluating CRC patient prognosis and treatment response was established based on the 18-CRSRGP signature and nomogram.

Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study.

BackgroundLung cancer is the leading cause of cancer-related mortality, and accurate prediction of patient survival can aid treatment planning and potentially improve outcomes. In this study, we proposed an automated system capable of lung segmentation and survival prediction using graph convolution neural network (GCN) with CT data in non-small cell lung cancer (NSCLC) patients.MethodsIn this retrospective study, we segmented 10 parts of the lung CT images and built individual lung graphs as inputs to train a GCN model to predict 5-year overall survival. A Cox proportional-hazard model, a set of machine learning (ML) models, a convolutional neural network based on tumor (Tumor-CNN), and the current TNM staging system were used as comparison.FindingsA total of 1,705 patients (main cohort) and 125 patients (external validation cohort) with lung cancer (stages I and II) were included. The GCN model was significantly predictive of 5-year overall survival with an AUC of 0.732 (p < 0.0001). The model stratified patients into low- and high-risk groups, which were associated with overall survival (HR = 5.41; 95% CI:, 2.32–10.14; p < 0.0001). On external validation dataset, our GCN model achieved the AUC score of 0.678 (95% CI: 0.564–0.792; p < 0.0001).InterpretationThe proposed GCN model outperformed all ML, Tumor-CNN, and TNM staging models. This study demonstrated the value of utilizing medical imaging graph structure data, resulting in a robust and effective model for the prediction of survival in early-stage lung cancer.

International Multicenter Study of Clinical Outcomes of Sinonasal Melanoma Shows Survival Benefit for Patients Treated with Immune Checkpoint Inhibitors and Potential Improvements to the Current TNM Staging System.

Objectives Sinonasal mucosal melanoma (SNMM) is an extremely rare and challenging sinonasal malignancy with a poor prognosis. Standard treatment involves complete surgical resection, but the role of adjuvant therapy remains unclear. Crucially, our understanding of its clinical presentation, course, and optimal treatment remains limited, and few advancements in improving its management have been made in the recent past. Methods We conducted an international multicenter retrospective analysis of 505 SNMM cases from 11 institutions across the United States, United Kingdom, Ireland, and continental Europe. Data on clinical presentation, diagnosis, treatment, and clinical outcomes were assessed. Results One-, three-, and five-year recurrence-free and overall survival were 61.4, 30.6, and 22.0%, and 77.6, 49.2, and 38.3%, respectively. Compared with disease confined to the nasal cavity, sinus involvement confers significantly worse survival; based on this, further stratifying the T3 stage was highly prognostic ( p < 0.001) with implications for a potential modification to the current TNM staging system. There was a statistically significant survival benefit for patients who received adjuvant radiotherapy, compared with those who underwent surgery alone (hazard ratio [HR] = 0.74, 95% confidence interval [CI]: 0.57-0.96, p = 0.021). Immune checkpoint blockade for the management of recurrent or persistent disease, with or without distant metastasis, conferred longer survival (HR = 0.50, 95% CI: 0.25-1.00, p = 0.036). Conclusions We present findings from the largest cohort of SNMM reported to date. We demonstrate the potential utility of further stratifying the T3 stage by sinus involvement and present promising data on the benefit of immune checkpoint inhibitors for recurrent, persistent, or metastatic disease with implications for future clinical trials in this field.

Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy

To develop a nomogram to predict the long-term survival of patients with esophageal cancer following esophagectomy. We collected the data of 7215 patients with esophageal carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database during the period from 2004 and 2016. Of these patients, 5052 were allocated to the training cohort and the remaining 2163 patients to the internal validation cohort using bootstrap resampling, with another 435 patients treated in the Department of Cardiothoracic Surgery of Jinling Hospital between 2014 and 2016 serving as the external validation cohort. In the overall cohort, the 1-, 3-, and 5-year cancer-specific mortality rates were 14.6%, 35.7% and 41.6%, respectively. Age (≥80 years vs < 50 years, P < 0.001), gender (male vs female, P < 0.001), tumor site (lower vs middle segment, P=0.013), histology (EAC vs ESCC, P=0.012), tumor grade (poorly vs well differentiated, P < 0.001), TNM stage (Ⅳ vs Ⅰ, P < 0.001), tumor size (> 50 mm vs 0-20 mm, P < 0.001), chemotherapy (yes vs no, P < 0.001), and LNR (> 0.25 vs 0, P < 0.001) were identified as independent risk factors affecting long-term survival of the patients. The nomograms established based on the model for predicting the survival probability of the patients at 1, 3 and 5 years after operation showed a C-index of 0.726 (95% CI: 0.714-0.738) for predicting the overall survival (OS) and of 0.735 (95% CI: 0.727-0.743) for cancer-specific survival (CSS) in the training cohort. In the internal validation cohort, the C-index of the nomograms was 0.752 (95% CI: 0.738-0.76) for OS and 0.804 (95% CI: 0.790-0.817) for CSS, as compared with 0.749 (95% CI: 0.736-0.767) and 0.788 (95%CI: 0.751-0.808), respectively, in the external validation cohort. The nomograms also showed a higher sensitivity than the TNM staging system for predicting long-term prognosis. This prognostic model has a high prediction efficiency and can help to identify the high-risk patients with esophageal carcinoma after surgery and serve as a supplement for the current TNM staging system.

Current controversies in TNM for the radiological staging of rectal cancer and how to deal with them: results of a global online survey and multidisciplinary expert consensus

ObjectivesTo identify the main problem areas in the applicability of the current TNM staging system (8th ed.) for the radiological staging and reporting of rectal cancer and provide practice recommendations on how to handle them.MethodsA global case-based online survey was conducted including 41 image-based rectal cancer cases focusing on various items included in the TNM system. Cases reaching < 80% agreement among survey respondents were identified as problem areas and discussed among an international expert panel, including 5 radiologists, 6 colorectal surgeons, 4 radiation oncologists, and 3 pathologists.ResultsThree hundred twenty-one respondents (from 32 countries) completed the survey. Sixteen problem areas were identified, related to cT staging in low-rectal cancers, definitions for cT4b and cM1a disease, definitions for mesorectal fascia (MRF) involvement, evaluation of lymph nodes versus tumor deposits, and staging of lateral lymph nodes. The expert panel recommended strategies on how to handle these, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define MRF involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes.ConclusionsThe recommendations derived from this global survey and expert panel discussion may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting.Key Points• Via a case-based online survey (incl. 321 respondents from 32 countries), we identified 16 problem areas related to the applicability of the TNM staging system for the radiological staging and reporting of rectal cancer.• A multidisciplinary panel of experts recommended strategies on how to handle these problem areas, including advice on cT-stage categorization in case of involvement of different layers of the anal canal, specifications on which structures to include in the definition of cT4b disease, how to define mesorectal fascia involvement by the primary tumor and other tumor-bearing structures, how to differentiate and report lymph nodes and tumor deposits on MRI, and how to anatomically localize and stage lateral lymph nodes.• These recommendations may serve as a practice guide and support tool for radiologists (and other clinicians) involved in the staging of rectal cancer and may contribute to improved consistency in radiological staging and reporting.

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy

BackgroundThe tumor microenvironment (TME) plays an important role in the occurrence and development of gastric cancer (GC) and is widely used to assess the treatment outcomes of GC patients. Immunohistochemistry (IHC) and gene sequencing are the main analysis methods for the TME but are limited due to the subjectivity of observers, the high cost of equipment and the need for professional analysts.MethodsThe ImmunoScore (IS) was developed in the TCGA cohort and validated in GEO cohorts. The Radiomic ImmunoScore (RIS) was developed in the TCGA cohort and validated in the Nanfang cohort. A nomogram was developed and validated in the Nanfang cohort based on RIS and clinical features.ResultsFor IS, the area under the curves (AUCs) were 0.798 for 2-year overall survival (OS) and 0.873 for 4-year overall survival. For RIS, in the TCGA cohort, the AUCs distinguishing High-IS or Low-IS and predicting prognosis were 0.85 and 0.81, respectively; in the Nanfang cohort, the AUC predicting prognosis was 0.72. The nomogram performed better than the TNM staging system according to the ROC curve (all P < 0.01). Patients with TNM stage II and III in the High-nomogram group were more likely to benefit from adjuvant chemotherapy than Low-nomogram group patients.ConclusionsThe RIS and the nomogram can be used to assess the TME, prognosis and adjuvant chemotherapy benefit of GC patients after radical gastrectomy and are valuable additions to the current TNM staging system. High-nomogram GC patients may benefit more from adjuvant chemotherapy than Low-nomogram GC patients.

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients. MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups. ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.

Association of Extrarenal Invasion Patterns and Tumor Size with the Differences in Survival Outcomes of T3a Renal Cell Carcinoma: A Proposal Modified T3a Stage System is Needed.

ObjectiveT3a renal cell carcinoma (RCC) did not consider tumor size and different extrarenal invasion patterns in the current TNM staging system. Here, we want to investigate the association of survival outcomes with different extrarenal invasion patterns and tumor size of T3a RCC.MethodsWe identified T3a RCC patients from the Surveillance, Epidemiology, and End Results database in 2004–2015. The extrarenal invasion patterns included renal vein invasion, renal sinus/peri-sinus fat invasion, or perinephric fat invasion. Cox proportional hazards models and Fine and Gray models were used to estimate overall survival (OS) and cancer-specific survival (CSS), and the hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. C-index is used to evaluate the predictive ability of the model. Restricted cubic splines were used to estimate the HRs of tumor size on the risk of OS and CSS.ResultsIn total, 4834 T3a RCC patients were included in the analysis. Of them, 1403 (29%) present isolated extrarenal invasion pattern, while 1403 (71%) were any combined invasion pattern, which was associated with a higher risk of lymph-node/distant metastasis and a worse OS and CSS compared with isolated extrarenal invasion pattern, but a comparable CSS and OS were observed between each isolated invasion pattern. In restricted cubic splines, a non-linear shape was observed for the association between the tumor size and OS and CSS, compared with the smallest tumor size group (≤4cm), the adjusted HR of the largest tumor size group (≥10cm) was 1.59 for all-cause mortality, and 2.27 for tumor-specific mortality, respectively. However, in a cohort of T3a RCC with a combined invasion pattern, tumor size is not an independent risk factor for prognosis. Finally, the model added two covariates of tumor size and invasion patterns that could improve the predictive ability of OS and CSS for T3a patients (c-index: +1.2%, +1.3%, respectively).ConclusionTumor size and extrarenal invasion type are valid parameters of the OS and CSS for T3a RCC patients and need to be considered for the next generation of the T-stage system.

Prognostic and staging value of tumor deposits in patients with rectal cancer after neoadjuvant chemoradiotherapy.

BackgroundThe presence of tumor deposits (TDs) is only considered in the absence of lymph node metastases (LNMs) in the current TNM staging system. However, the prognostic value of TDs when concomitant with LNM for rectal cancer after neoadjuvant chemoradiotherapy (NCRT) remains unclear. This study aimed to evaluate the prognostic value of TDs and when concomitant with LNMs in rectal cancer after NCRT.MethodsPatients with rectal cancer who had received NCRT between 2010 and 2016 were obtained from the Surveillance, Epidemiology and End Results (SEER) 18 (year range, 1975–2016) database. Data were extracted on the following: age, sex, race, TNM stage, total LNs harvested, positive LNs, histologic type, perineural invasion, grade, carcinoma embryonic antigen status, TD number, and cancer-specific survival (CSS) rates. The primary objective was to determine the prognostic impact of TDs on CSS. The effect of the addition of TD to the LNM count for a novel N stage was also evaluated. Univariate and multivariate analyses were performed using the Kaplan-Meier method and Cox models.ResultsOf 9,620 patients, 865 (9.0%) had TDs. TD-positive patients showed a worse prognosis than TD-negative patients (HR =2.39, 95% CI: 2.04–2.80, P<0.001), and multivariate analysis showed that the presence of TDs was an independent poor prognostic factor (HR =1.41, 95% CI: 1.19–1.67, P<0.001). Regarding the LN status, TDs were associated with a higher risk of cancer-specific death in the LNM- group (HR =2.43, 95% CI: 1.86–3.18, P<0.001), M1 group (HR =1.51, 95% CI: 1.08–2.10, P<0.001), and ypN1 group (HR =2.08, 95% CI: 1.61–2.70, P<0.001), but not in the ypN2 group (HR =0.97, 95% CI: 0.69–1.36, P=0.84). Patients with concomitant TDs and LNM showed significantly worse survival than those with TDs or LNM alone (5-year CSS: 48.2%, 72.2%, and 67.8%, respectively). The 5-year CSS rates were 86.2%, 77.4%, 65.1%, 53.8%, and 46.5% for the novel N0, N1a, N1b, N2a, and N2b groups, respectively (P<0.05 across all groups). Time dependent receiver operating characteristic curve analysis and decision curve analysis showed that the novel N stage was superior to the current ypN stage.ConclusionsThe presence of TDs is an independent poor prognostic factor for LARC patients after NCRT. The concomitant presence of TDs and LNM indicates a significantly worse survival, and the addition of TD to LNM may help to better prompt appropriate risk stratification.