Addition of sintilimab to nanoparticle albumin-bound paclitaxel and S-1 as adjuvant therapy in stage IIIC gastric cancer

The prognosis of stage III gastric cancer (GC) is not satisfying and the specific chemotherapy regimens for GC of stage IIIC based on the 8th edition of the UICC/AJCC TNM staging system are still inconclusive. Peritoneal recurrence is the common and severe relapse pattern. Nanoparticle albumin-bound paclitaxel (Nab-PTX) is safer and more effective than PTX in the peritoneal metastasis. Clinical trial has demonstrated the safety and efficacy of sintilimab in GC. A combination of Nab-PTX, S-1 and sintilimab could be a promising triplet regimen as adjuvant therapy for GC. The aim of this article is to describe the design of this prospective Dragon-VII trial, conducted to evaluate the safety and efficacy of the combination of Nab-PTX, S-1 and sintilimab. Trial registration: NCT04781413

Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

Background Primary bladder sarcoma (PBS) is a rare malignant tumor of the bladder with a poor prognosis, and its disease course is inadequately understood. Therefore, our study aimed to establish a prognostic model to determine individualized prognosis of patients with PBS. Patients and Methods Data of 866 patients with PBS, registered from 1973 to 2015, were extracted from the surveillance, epidemiology, and end result (SEER) database. The patients included were randomly split into a training (n = 608) and a validation set (n = 258). Univariate and multivariate Cox regression analyses were employed to identify the important independent prognostic factors. A nomogram was then established to predict overall survival (OS). Using calibration curves, receiver operating characteristic curves, concordance index (C-index), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), the performance of the nomogram was internally validated. We compared the nomogram with the TNM staging system. The application of the risk stratification system was tested using Kaplan–Meier survival analysis. Results Age at diagnosis, T-stage, N-stage, M-stage, and tumor size were identified as independent predictors of OS. C-index of the training cohort were 0.675, 0.670, 0.671 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.701, 0.684, 0.679, respectively. Calibration curves also showed great prediction accuracy. In comparison with TNM staging system, improved net benefits in DCA, evaluated NRI and IDI were obtained. The risk stratification system can significantly distinguish the patients with different survival risk. Conclusion A prognostic nomogram was developed and validated in the present study to predict the prognosis of the PBS patients. It may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Modified cancer TNM classification for localized renal cell carcinoma based on the prognostic analysis of 3748 cases from a single center

The optimal cutoff point for evaluating the prognosis of localized renal cell carcinoma (LRCC) remains unclear. This study aimed to verify the efficacy of tumor diameter in the 2010 American Joint Committee on Cancer (AJCC) TNM staging system and contribute to the modification of TNM staging on the prognosis of this disease. A total of 3748 patients with LRCC were enrolled and grouped according to the 2010 AJCC TNM staging system. COX analysis was used to stratify the prognosis. The optimal cutoff point of the tumor diameter in the T1 and T2 prognosis was explored. There were 3330 (88.9%) patients in stage T1 and 418 (11.1%) in stage T2. The cancer-specific mortality rate was 2.7% (100/3748). The mean follow-up was 49.8 months. A tumor diameter of 7 cm can determine the prognosis of patients at stages T1 and T2; however, 4.5 cm and 11 cm as the cutoff points for T1 and T2 sub-classification of patients with LRCC might show better recognition ability than 4 cm and 10 cm, respectively. The 2010 AJCC TNM stage can predict the prognosis of LRCC in stages T1 and T2. In addition, a tumor diameter of 4.5 cm and 11 cm might be the optimal cutoff points for the sub-classification of stages T1 and T2.

A Nomogram Based on the Log Odds of Positive Lymph Nodes Predicts the Prognosis of Patients With Distal Cholangiocarcinoma After Surgery

Background: Lymph node (LN) metastasis is considered one of the most important risk factors affecting the prognosis of distal cholangiocarcinoma (DCC). This study aimed to demonstrate the superiority of log odds of positive lymph nodes (LODDS) compared with other LN stages, and to establish a novel prognostic nomogram to predict the cancer-specific survival (CSS) of DCC.Methods: From the Surveillance, Epidemiology and End Results (SEER) database, the data of 676 patients after DCC radical operation were screened, and patients were randomly divided into training (n = 474) and validation sets (n = 474). The prognostic evaluation performance of the LODDS and American Joint Commission on Cancer (AJCC) N stage and lymph node ratio (LNR) were compared using the Akaike information criteria, receiver operating characteristic area under the curve (AUC), and C-index. Multivariate Cox analysis was used to screen independent risk factors, and a LODDS-based nomogram prognostic staging model was established. The nomogram's precision was verified by C-index, calibration curves, and AUC, and the results were compared with those of the AJCC TNM staging system.Results:Compared with the other two stages of LN metastasis, LODDS was most effective in predicting CSS in patients with DCC. Multivariate analysis proved that LODDS, histologic grade, SEER historic stage, and tumor size were independent risk factors for DCC. The C-index of the nomogram, based on the above factors, in the validation set was 0.663. The 1-, 3-, and 5-y AUCs were 0.735, 0.679, and 0.745, respectively. Its good performance was also verified by calibration curves. In addition, the C-index and Kaplan-Meier analysis showed that the nomogram performed better than the AJCC TNM staging system.Conclusion:For postoperative patients with DCC, the LODDS stage yielded better prognostic efficiency than the AJCC N and LNR stages. Compared with the AJCC TNM staging system, the nomogram, based on the LODDS, demonstrated superior performance.

Masaoka-Koga and TNM Staging System in Thymic Epithelial Tumors: Prognostic Comparison and the Role of the Number of Involved Structures

Background: The aim of this study was to evaluate the Masaoka–Koga and the tumor node metastases (TNM) staging system in thymic epithelial tumors (TET) considering possible improvements. Methods: We reviewed the data of 379 patients who underwent surgical resection for TET from 1 January 1985 to 1 January 2018, collecting and classifying the pathological report according to the Masaoka–Koga and the TMN system. The number of involved organs was also considered as a possible prognostic factor and integrated in the two staging systems to verify its impact. Results: Considering the Masaoka–Koga system, 5- and 10-year overall survival (5–10YOS) was 96.4% and 88.9% in stage I, 95% and 89.5% in stage II and 85.4% and 72.8% in stage III (p = 0.01), with overlapping in stage I and stage II curves. Considering the TNM system, 5–10YOS was 95.5% and 88.8% in T1, 84.8% and 70.7% in T2 and 88% and 76.3% in T3 (p = 0.02), with overlapping T2–T3 curves. Including the number of involved structures, in Masaoka–Koga stage III, patients with singular involved organs had a 100% and 76.6% vs. 87.7% 5–10YOS, which was 76.6% in patients with multiple organ infiltration. Considering the TNM, T3 patients with singular involved structures presented a 5–10YOS of 100% vs. 62.5% and 37.5% in patients with multiple organ involvement (p = 0.07). Conclusion: The two staging systems present limitations due to overlapping curves in early Masaoka–Koga stages and in advanced T stages for TNM. The addition of the number of involved organs seems to be a promising factor for the prognosis stratification in these patients.

Boosting Advanced Nasopharyngeal Carcinoma Stage Prediction Using a Two-Stage Classification Framework Based on Deep Learning

Nasopharyngeal carcinoma (NPC) is a popular malignant tumor of the head and neck which is endemic in the world, more than 75% of the NPC patients suffer from locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The survival quality of these patients depends on the reliable prediction of NPC stages III and IVa. In this paper, we propose a two-stage framework to produce the classification probabilities for predicting NPC stages III and IVa. The preprocessing of MR images enhance the quality of images for further analysis. In stage one transfer learning is used to improve the classification effectiveness and the efficiency of CNN models training with limited images. Then in stage two the output of these models are aggregates using soft voting to boost the final prediction. The experimental results show the preprocessing is quite effective, the performance of transfer learning models perform better than the basic CNN model, and our ensemble model outperforms the single model as well as traditional methods, including the TNM staging system and the Radiomics method. Finally, the prediction accuracy boosted by the framework is, respectively, 0.81, indicating that our method achieves the SOTA effectiveness for LA-NPC stage prediction. In addition, the heatmaps generated with Class Activation Map technique illustrate the interpretability of the CNN models, and show their capability of assisting clinicians in medical diagnosis and follow-up treatment by producing discriminative regions related to NPC in the MR images. Graphic Abstract

Volumetric staging in radiotherapy for oropharyngeal cancers

Introduction: Despite routine use of 3D radiotherapy planning in radical radio(chemo)therapy for oropharyngeal cancers, volumetric data have not been implemented in initial staging. We analyzed 228 oropharyngeal cancer cases treated at one institution between 2004 and 2014 to compare the predictive value of volumetric staging and tumor nodal metastasis staging system (TNM) and determine whether they could be complementary for the estimation of survival. Methods: This retrospective study analyzed 228 consecutive oropharyngeal cancer cases treated with radiotherapy (76.9%) or concurrent radiochemotherapy (23.1%) between 2004 and 2014. The volumetric parameters included primary gross tumor volume (pGTV), metastatic lymph nodes gross tumor volume (nGTV), and total gross tumor volume (tGTV), and were compared with the 7th edition of the TNM staging system. Results: Median overall survival (OS) was 30.3 months. In the receiver operating characteristic analysis, tGTV had the highest area under the curve (AUC) of 0.66, followed by pGTV (AUC,0.64), nGTV (AUC 0.62), and TNM (AUC 0.6). The median OS for patients with tGTV ⩽32.2 mL was 40.5 months, compared to 15.4 months for >32.2 mL ( p < 0.001). This threshold allowed for a statistically significant difference in survival between TNM stage IV cases with low and high tumor volume ( p < 0.001). Despite both TNM and tGTV reaching statistical significance in univariate analysis, only the tGTV remained an independent prognostic factor in the multivariate analysis (hazard ratio 1.07, confidence interval 1.02–1.12, p = 0.008). Conclusions: tGTV is an independent prognostic factor, characterized by a higher discriminatory value than the TNM staging system, and can be used to further divide stage IV cases into subgroups with significantly different prognosis.

Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning

Background Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. Methods and findings Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10−23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman’s rank correlation coefficient = 0.8316; p-value = 4.5×10−13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10−43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. Conclusions The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

A novel pN3 gastric cancer staging system with superior prognostic utility based upon the examination of over 31 lymph nodes: a propensity score-matching analysis

Background Individuals with pN3 gastric cancer (GC) account for a large proportion of pN + GC, and exhibit poor survival outcomes. The pN3 stage is defined based upon the number of metastatic lymph nodes (mLNs), but the subclassification of pN3 patients based upon the number of examined LNs (eLNs) is rarely performed. Methods In total, 2894 pTxN3M0 GC patients in the Surveillance, Epidemiology, and End Results database that had undergone surgery from 2000 to 2016 were selected for analysis. The X-tile software was used to select the optimal cutoff values. Cox proportional regression analyses were used to evaluated hazard ratios corresponding to the risk of death. Selection bias was minimized via propensity score matching (PSM). Results As the number of eLNs rose, the risk of death for patients trended downwards. Survival analyses indicated that patients with ≤ 31 eLNs exhibited significantly poorer survival outcomes as compared to patients with > 31 eLNs (5-year OS: 18.4% vs. 24.7%), and this result remained significant when analyzing 857 pairs of patients following PSM analysis. Significant differences in prognosis were additionally observed when comparing pN3a and pN3b patients with ≤ 31 or > 31 eLNs under pT3/4a stage. For pT4b stage, pN3a patients with > 31 eLNs also exhibited a better prognosis than other patients. The novel TNM staging system designed exhibited excellent utility as a tool for the prognostic evaluation of this GC patient population. Conclusions These results suggest that in pN3 GC, a minimum of 32 LNs should be examined. The novel TNM staging system for pN3 patients described herein, which was developed based upon the number of eLNs, may thus be of value in clinical settings.