The Critical Role of Tumor Size in Predicting Prognosis for T1 Colon Cancer

Association between tumor size and prognosis in patients with small bowel adenocarcinoma-a SEER-based study

Background

The association between small-bowel adenocarcinoma (SBA) tumor size and prognosis is unclear, and we used the Surveillance, Epidemiology, and End Results (SEER) database to assess the prognostic value of SBA tumor size.

Methods

Patients with postoperative SBA were selected from the SEER database, and overall survival (OS) and cancer-specific survival (CSS) were used as outcome variables. Tumor size was used as a categorical and continuous variable, respectively, to adjust for confounders and analyze the association between SBA tumor size and prognosis using Cox proportional hazard regression, and the results were visualized using restricted cubic splines (RCS). Spearman correlation coefficient was used to evaluate the statistical correlation between tumor size and tumor invasion depth (T-stage). Kaplan-Meier survival curves were used to estimate OS at different T stages.

Results

When the tumor size was analyzed as a quantitative variable, the adjusted covariate model showed that the HR was 1.008 (P = 0.04) for OS and 1.021 (P = 0.03) for CSS. And regardless of OS or CSS, when the tumor size < 3-4 cm, there was a close linear relationship between tumor size and HR. What's more, in the SEER database, the 5-year survival rates of T1, T2, T3 and T4 patients were 81.8 %, 81.1 %, 66.0 % and 50.9 % (P < 0.001) according to AJCC T-stage. However, in the modified T-stage (mT), these rates were 82.8 %, 70.6 %, 60.7 % and 39.8 % (P < 0.001). When patients within each of the AJCC T stages were stratified by mT stages, significant survival heterogeneity was observed within each of the AJCC T1 to T4 stages(P < 0.001).

Conclusion

When tumor size is used in a quantitative way, tumor size is an independent predictor of poor outcome in patients with SBA. Furthermore, we established a modified T-stage based on tumor size and depth of invasion.

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer

OBJECTIVE

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging.

METHODS

Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification.

RESULTS

While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata.

CONCLUSIONS

Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.

Low muscle mass-to-fat ratio is an independent factor that predicts worse overall survival and complications in patients with colon cancer: a retrospective single-center cohort study

Purpose

This study was performed to investigate influencing factors of preoperative muscle mass-to-fat ratio (MMFR) and its impact on overall survival and postoperative complications of colon cancer.

Methods

Patients who underwent colectomy for stage I-III colon cancer at the Second Affiliated Hospital of Soochow University between January 2016 and December 2022 were included. The skeletal muscle and fat area at the third lumbar vertebra were measured with preoperative CT measurement. MMFR was defined as the ratio of skeletal muscle area to total fat area, and low MMFR was defined as the 2 lowest tertiles (≤0.585). Univariate and multivariable analyses were conducted to assess the impact of MMFR on overall complications and survival outcomes. Kaplan-Meier survival curves and log-rank test were used to compare the overall survival between high MMFR and low MMFR groups.

Results

A total of 885 patients were analyzed. Female sex, older age, high body mass index, sarcopenia, and high cancer stage were more likely to result in low MMFR. Complications, including intestinal fistula, chylous fistula and organ space surgical site infection were significantly higher in the low MMFR group. Low MMFR was an independent factor associated with overall complications (odds ratio, 1.940; 95% confidence interval [CI], 1.252-3.007; P < 0.01) and long-term survival (hazard ratio, 2.222; 95% CI, 1.443-3.425; P < 0.01). Furthermore, patients with high MMFR had a higher survival rate than patients with low MMFR (P < 0.01).

Conclusion

Low MMFR is an independent factor that predicts worse overall survival and complications in patients with colon cancer.

The impact of tumor size on the prognosis and chemotherapy efficacy in stage I/II colon cancer patients

Background: The impact of tumor size on the survival and chemotherapy reponse of early-stage colon cancer remains unclear. Our study explored the effect of tumor size on overall survival (OS) and postoperative chemotherapy efficacy in patients with stage I/II colon cancer. Methods: Stage I/II colon cancer patients from the Surveillance, Epidemiology and End Results (SEER) database and a China center were extracted as two cohorts respectively. X-tile program was adopted to acquire optimal cutoff points of tumor size (16mm and 49mm). Harrell's concordance index (c-index) and time-dependent receiver operating characteristic curve (ROC) were used to indicate discrimination ability of prognostic factors. Results: Overall, 104,908 and 168 stage I/II postoperative colon cancer patients from SEER database and a China center were eligible, respectively. Kaplan-Meier analysis showed that large tumor size was associated with poor OS in two cohorts. The effect of tumor size on OS gradually decreased as the T stage increased both before PSM (c-index 0.535 for T1N0M0 and 0.506 for T4N0M0, p<0.05) and after PSM (c-index 0.543 for T1N0M0, p<0.05; c-index 0.543 for T4N0M0, p>0.05). Stratified analyses showed that chemotherapy improved the OS rate by 9.5% (chemotherapy vs. non-chemotherapy: 83.5% vs. 73.0%) or 12.8% (chemotherapy vs. non-chemotherapy: 85.7% vs. 72.9%) before and after PSM in T2N0M0 patients with tumor size >49 mm, but not in T1N0M0. The survival benefit provided by chemotherapy for T2N0M0 patients with large tumor was also validated in the Chinese cohort. Conclusions: Large tumor size was a risk factor for stage I/II colon cancer, especially for T1N0M0. Tumor size could serve as a complementary factor guiding postoperative chemotherapy for T2N0M0 patients.

Prognostic values of tumor size and location in early stage endometrial cancer patients who received radiotherapy

OBJECTIVE

To investigate the correlation between tumor size, tumor location, and prognosis in patients with early-stage endometrial cancer (EC) receiving adjuvant radiotherapy.

METHODS

Data of patients who had been treated for stage I-II EC from March 1999 to September 2017 in 13 tertiary hospitals in China was screened. Cox regression analysis was performed to investigate associations between tumor size, tumor location, and other clinical or pathological factors with cancer-specific survival (CSS) and distant metastasis failure-free survival (DMFS). The relationship between tumor size as a continuous variable and prognosis was demonstrated by restricted cubic splines. Prognostic models were constructed as nomograms and evaluated by Harrell's C-index, calibration curves and receiver operating characteristic (ROC) curves.

RESULTS

The study cohort comprised 805 patients with a median follow-up of 61 months and a median tumor size of 3.0 cm (range 0.2-15.0 cm). Lower uterine segment involvement (LUSI) was found in 243 patients (30.2%). Tumor size and LUSI were identified to be independent prognostic factors for CSS. Further, tumor size was an independent predictor of DMFS. A broadly positive relationship between poor survival and tumor size as a continuous variable was visualized in terms of hazard ratios. Nomograms constructed and evaluated for CSS and DMFS had satisfactory calibration curves and C-indexes of 0.847 and 0.716, respectively. The area under the ROC curves for 3- and 5-year ROC ranged from 0.718 to 0.890.

CONCLUSION

Tumor size and LUSI are independent prognostic factors in early-stage EC patients who have received radiotherapy. Integrating these variables into prognostic models would improve predictive ability.

Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study

BACKGROUND

An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making.

METHODS

We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

RESULTS

The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS.

CONCLUSIONS

In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database

PURPOSE

To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model.

METHODS

Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group.

RESULTS

Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects.

CONCLUSION

Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.

Prognostic Value of Tumor Size in Colon Cancer-Smaller is Better?

BACKGROUND

The prognostic value of tumor size in colon cancer remains controversial. This study aimed to reveal the correlation between tumor size and prognosis of colon cancer.

METHODS

A total of 491 patients with colon cancer were included in this study. The correlation of tumor size with prognosis, mismatch repair status, and other clinicopathological characteristics as well as tumor microenvironment was analyzed.

RESULTS

For stage IIA microsatellite stable (MSS) colon cancer, tumors sized <3.5 cm and ≥5 cm were associated with a poorer disease free survival (DFS) compared with tumors sized between 3.5 and 5 cm (P = .002). Small tumor size (HR = 5.098, P = .001) and large tumor size (HR = 2.749, P = .029) were found to be independent prognostic factors for stage IIA MSS colon cancer. Moreover, high expression of transgelin (TAGLN), a marker of cancer-associated fibroblasts (CAFs), was found to be an independent prognostic factor for poorer DFS (HR = 9.651, P = .009), which was also associated with smaller tumor size (P = .027).

CONCLUSION

Small (<3.5 cm) and large (≥5 cm) tumor sizes are associated with decreased DFS in stage IIA MSS colon cancer. Enrichment of TAGLN+ CAFs is associated with decreased DFS and small tumor size.

Random Lasing for Bimodal Imaging and Detection of Tumor

The interaction of light with biological tissues is an intriguing area of research that has led to the development of numerous techniques and technologies. The randomness inherent in biological tissues can trap light through multiple scattering events and provide optical feedback to generate random lasing emission. The emerging random lasing signals carry sensitive information about the scattering dynamics of the medium, which can help in identifying abnormalities in tissues, while simultaneously functioning as an illumination source for imaging. The early detection and imaging of tumor regions are crucial for the successful treatment of cancer, which is one of the major causes of mortality worldwide. In this paper, a bimodal spectroscopic and imaging system, capable of identifying and imaging tumor polyps as small as 1 mm2, is proposed and illustrated using a phantom sample for the early diagnosis of tumor growth. The far-field imaging capabilities of the developed system can enable non-contact in vivo inspections. The integration of random lasing principles with sensing and imaging modalities has the potential to provide an efficient, minimally invasive, and cost-effective means of early detection and treatment of various diseases, including cancer.

Prognostic Potential of Nectin Expressions in Colorectal Cancer: An Exploratory Study

Colorectal cancer (CRC) is a pressing global health challenge, with an estimated 1.9 million new cases in 2020. Ranking as the third most diagnosed cancer globally, CRC accounts for nearly 930,000 cancer-related deaths annually. Nectins, immunoglobulin-like adhesion molecules, are pivotal in intercellular adhesion formation and cellular function regulation. Altered nectin expression patterns have been identified in various cancers. However, the intricacies of their role in cancer development and progression remain underexplored. This study aimed to evaluate the expression of specific nectins in CRC tumors, explore their association with clinicopathological factors, and ascertain their potential as prognostic indicators for CRC patients post-resection. We retrospectively analyzed the medical records of 92 CRC patients who underwent surgical treatment between 2013 and 2014. Tumor specimens were re-evaluated to determine nectin expression using immunohistochemistry. The study identified heterogeneous expressions of nectin-2, -3, and -4 in 58%, 62.6%, and 87.9% of specimens, respectively. Elevated nectin-4 expression correlated with worse 5-year and overall survival rates, presenting a negative prognostic value (HR = 4, 95% CI: 2.4-6.8, p < 0.001). Conversely, reduced nectin-3 expression was linked to poorer CRC prognosis (HR = 0.54; 95% CI: 0.31-0.96; p = 0.036). Nectin-4 expression positively correlated with elevated carcinoembryonic antigen (CEA) levels and advanced disease stages. In contrast, nectin-3 expression negatively correlated with CEA levels, tumor size, presence of distant metastases, and disease stage. Notably, tumors in the right colon were statistically more likely to express nectin-2 compared to those in the left. This study underscores the potential prognostic significance of nectins in CRC. The high prevalence of nectin-4-expressing cells offers promising avenues for further evaluation in targeted therapeutic interventions with already available agents such as PADCEV.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

A Nomogram Model for Stratifying the Risk of Recurrence in Patients with Meningioma After Surgery

BACKGROUND

Here, we aimed to investigate the clinical parameters affecting the recurrence of meningiomas, and to construct a predictive nomogram model, so as to predict the recurrence-free survival (RFS) of meningiomas more accurately.

METHODS

The Clinical, imaging, and pathological data of 155 primary meningioma patients treated surgically from January 2014 to March 2021 were retrospectively analyzed. Independent prognostic factors affecting postoperative recurrence of meningioma were identified by univariate and multivariate Cox regression analyses. A predictive nomogram was established based on independent influence parameters. Subsequently, time-dependent receiver operating characteristic curve, calibration curve, and Kaplan-Meier method were utilized to evaluate the predictive ability of the model.

RESULTS

The multivariate Cox regression analysis showed that tumor size, Ki-67 index, and resection extent had independent prognostic significance, and these parameters were subsequently used to construct a predictive nomogram. Receiver operating characteristic curves indicated that the model was more accurate in predicting RFS than independent factors. Calibration curves suggested that the predicted RFS were similar to the actual observed RFS. In the Kaplan-Meier analysis, the RFS of high-risk cases was obviously shorter than that of low-risk cases.

CONCLUSIONS

The tumor size, Ki-67 index, and extent of resection were independent factors affecting the RFS of meningioma. The predictive nomogram based on these factors can be used as an effective method to stratify the recurrence risk of meningioma and provide a reference for patients to choose personalized treatment.

Prognostic impact of tumor size on patients with metastatic colorectal cancer: a large SEER-based retrospective cohort study

Given the poor prognosis of metastatic colorectal cancer (mCRC), this research aimed to investigate the correlation between tumor size and prognosis, and develop a novel prediction model to guide individualized treatment. Patients pathologically diagnosed with mCRC were recruited from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and were randomly divided (7:3 ratio) into a training cohort (n = 5597) and a validation cohort (n = 2398). Kaplan-Meier curves were used to analyze the relationship between tumor size and overall survival (OS). Univariate Cox analysis was applied to assess the factors associated with the prognosis of mCRC patients in the training cohort, and then multivariate Cox analysis was used to construct a nomogram model. The area under the receiver-operating characteristics curve (AUC) and calibration curve were used to evaluate the predictive ability of the model. Patients with larger tumors had a worse prognosis. While brain metastases were associated with larger tumors compared to liver or lung metastases, bone metastases tended to be associated with smaller tumors. Multivariate Cox analysis revealed that tumor size was an independent prognostic risk factor (HR 1.28, 95% CI 1.19-1.38), in addition to the other ten variables (age, race, primary site, grade, histology, T stage, N stage, chemotherapy, CEA level and metastases site). The 1-, 3-, and 5-year OS nomogram model yielded AUC values of more than 0.70 in both the training and validation cohorts, and its predictive performance was superior to that of the traditional TNM stage. Calibration plots demonstrated a good agreement between the predicted and observed 1-, 3-, and 5-year OS outcomes in both cohorts. The size of primary tumor was found to be significantly associated with prognosis of mCRC, and was also correlated with specific metastatic organ. In this study, we presented the first effort to create and validate a novel nomogram for predicting 1-, 3- and 5-year OS probabilities of mCRC. The prognostic nomogram was demonstrated to have an excellent predictive ability in estimating individualized OS of patients with mCRC.

A Retrospective Study of the Prognostic Patterns in Colorectal Cancer Patients

Introduction  Colorectal cancer (CRC) management has advanced globally, leading to a steady decline in mortality rates. However, recent studies have shown that the prognosis of CRC varies based on the anatomical site of the primary tumor, histopathological grading, and type of mutation. With an increase in the incidence of CRC globally and in Bahrain, there is a need for a recent descriptive study to improve overall management. This study aims to investigate the anatomical, histopathological, and molecular prognostic factors in CRC patients presenting to the Salmaniya Medical Complex (SMC). Methods  The study was conducted retrospectively using ISEHA electronic database over two years (January 2019 to December 2020). A total of 101 patients with primary CRC registered in the General Surgery Department were included in this study. The sample size was further stratified and analyzed using descriptive statistics based on the available data of measured outcomes. Results  Anatomical data showed that 65% of CRC patients had a tumor on the left side of the colon, 27.7% on the right side, and 7% in the transverse colon. Overall, 16.8% of all patients had rectal involvement. Histopathological data showed that 86% of the patients had a low-grade CRC adenocarcinoma. The most diagnosed tumor stage was pT3N0M0 (22.8%). In addition, there were ten metastatic cases (10 to the liver, of which three had concomitant lung involvement and two had concurrent brain metastases). The average tumor diameter was 46.2 mm, where 63% ranged between 30 mm to 69 mm. Most mutations involved the TP53 (27.7%) and the KRAS (29%) genes.  Conclusion  The study found that majority of CRC patients at SMC in Bahrain had relatively good overall anatomical, tumor staging and grading prognostic factors but somewhat poorer molecular prognostics.

Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

BACKGROUND

It has previously been observed that the prognostic value of tumor size varied according to different stages patients enrolled in gastric cancer. We aimed to investigate the influence of T stage on the prognostic and predicting value of tumor size.

MATERIAL AND METHODS

A total of 13,585 patients with stage I-III gastric cancer were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and multivariate cox regression analysis stratified by T stage were performed. C-index and time-dependent receiver operating characteristic curve (ROC) curve were applied to assess discrimination ability of tumor size and other factors. Nomograms were constructed to further assess the performance of tumor size in a specific model. Calibration ability, discrimination ability, reclassification ability and clinical benefits were executed to judge the performance of models.

RESULTS

Stratified analyses according to T stage illustrated that with the increase of T stage, the effect of tumor size on overall survival (OS) and cancer-specific survival (CSS) significantly decreased. Moreover, tumor size showed superior discrimination ability in T1 gastric cancer, outperformed other prognostic factors in predicting both CSS (C-index: 0.666, AUC: 0.687) and OS (C-index: 0.635, AUC: 0.660). The cox regression model included tumor size showed better performance than the model excluded tumor size in every aspect.

CONCLUSION

T stage had a negative impact on the predicting value of tumor size. Tumor size showed significant prognostic value in T1 gastric cancer, which may be effective in clinical practice.

The critical role of serum thymidine kinase 1(STK1) in predicting prognosis for immunotherapy in T4 stage lung squamous cell carcinoma

Purpose

The role of serum thymidine kinase 1 (STK1) in predicting the prognosis of T4-stage lung squamous cell carcinoma (LUSC) with immunotherapy is the focus of our work.

Methods

A total of 180 LUSC patients were enrolled. In this study, according to the T stage, the patients were divided into two groups: the T1-T2 stage and the T3-T4 stage. Receiver operating characteristic (ROC) curves were used to determine the best cutoff value for predicting overall survival (OS) outcomes. The next step is to use this cutoff value to introduce univariate and multivariate Cox regression models to screen the prognostic factors in different T stages of LUSC. The association of STK1 with other clinicopathological factors was also determined. Finally, to further explore the link between STK1 and the staging of LUSC patients, we have further divided the staging into T1-3 and T4 stages. We identified factors influencing the prognosis of patients who received immunotherapy in T4 stage LUSC.

Results

First, we determined that the optimal cutoff for STK1 for predicting OS outcome was 1.165 pmol/L. Correlation analysis revealed that STK1 was over-expressed in LUSC patients at the T3-4 stage. Univariate and multivariate analysis showed that immunotherapy was an independent prognostic factor in patients with T4 stage LUSC. In the group of patients who received immunotherapy or not, the STK1 expression level was found to be an independent prognostic factor in T4 LUSC patients receiving PD-1/PD-L1 inhibitor treatment; patients with high levels of STK1 had an increased risk of death (95%CI = 1.028-2.04).

Conclusion

STK1 is associated with a higher T stage and may be an effective prognostic marker for advanced LUSC immunotherapy patients.

Expression of CD22 in Triple-Negative Breast Cancer: A Novel Prognostic Biomarker and Potential Target for CAR Therapy

Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancer cases. Due to the lack of expression of well-known molecular targets [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)], there is a need for more alternative treatment approaches in TNBC. Chimeric antigen receptor (CAR)-T cell-based immunotherapy treatment is one of the latest treatment technologies with outstanding therapeutic advances in the past decade, especially in the treatment of hematologic malignancies, but the therapeutic effects of CAR-T cells against solid tumors have not yet shown significant clinical benefits. Identification of highly specific CAR-T targets in solid tumors is also crucial for its successful treatment. CD22 is reported to be a multifunctional receptor that is mainly expressed on the surface of mature B-cells (lymphocytes) and is also highly expressed in most B-cell malignancies. This study aimed to investigate the expression of CD22 in TNBC. Bioinformatic analysis was performed to evaluate the expression of CD22 in breast carcinoma and normal tissues. RNA-seq data of normal and breast carcinoma patients were downloaded from The Cancer Genome Atlas (TCGA), and differential gene expression was performed using R language. Additionally, online bioinformatics web tools (GEPIA and TNM plot) were used to evaluate the expression of CD22 in breast carcinoma and normal tissues. Western blot (WB) analysis and immunofluorescence (IF) were performed to characterize the expression of CD22 in TNBC cell lines. Immunohistochemical (IHC) staining was performed on tumor specimens from 97 TNBC patients for CD22 expression. Moreover, statistical analysis was performed to analyze the association of clinical pathological parameters with CD22 expression. Correlation analysis between overall survival data of TNBC patients and CD22 expression was also performed. Differential gene expression analysis of TCGA data revealed that CD22 is among the upregulated differentially expressed genes (DEGs) with high expression in breast cancer, as compared to normal breast tissues. WB and IF analysis revealed high expression of CD22 in TNBC cell lines. IHC results also showed that approximately 62.89% (61/97) of TNBC specimens were stained positive for CD22. Cell membrane expression of CD22 was evident in 23.71% (23/97) of TNBC specimens, and 39.18% (38/97) of TNBC specimens showed cytoplasmic/membrane expression, while 37.11% (36/97) specimens were negative for CD22. Furthermore, significant associations were found between the size of tumors in TNBC patients and CD22 expression, which unveils its potential as a prognostic biomarker. No significant correlation was found between the overall survival of TNBC patients and CD22 expression. In conclusion, we demonstrated for the first time that CD22 is highly expressed in TNBC. Based on our findings, we anticipated that CD22 could be used as a prognostic biomarker in TNBC, and it might be a potential CAR-T target in TNBC for whom few therapeutic options exist. However, more large-scale studies and clinical trials will ensure its potential usefulness as a CAR-T target in TNBC.

Local excision of T1 colorectal cancer: good differentiation, absence of lymphovascular invasion, and limited tumor radial infiltration (≤4.25 mm) may allow avoiding radical surgery

BACKGROUND

Early colorectal cancer (ECC) is defined as T1NXM0 colorectal cancer (CRC). Although a non-negligible number of T1-CRCs presents metastatic lymph-nodes, local excision is increasingly proposed as alternative to radical resection. Several criteria have been suggested to identify low-risk T1-CRC, but recommendations on this topic are still heterogeneous. This study aims to identify criteria associated with N+ T1-CRC, to select patients to undergo (or not) local excision.

METHODS

A retrospective analysis of demographic, clinical, and histology criteria of 122 consecutive T1-CRC patients undergoing radical resection at Parma University Hospital between 2000 and 2018 has been performed.

RESULTS

Lymph-node metastasis (LNM) was observed in 15/122 patients (12.3%). No LNM was observed among well-differentiated (G1) tumors (0/37), while 10/65 (15.4%) G2 cases as well as 5/20 (25%) G3 patients presented LNM. G1 was associated with absence of LNM (p = 0.013). After excluding G1 patients, the rate of N + T1-CRC was 17.6% (15/85). LNM was observed in 4/8 (50%) patients with lymphovascular invasion (LVI) and in 11/77 (14.2%) without LVI. LVI resulted being associated with LNM (p < 0.042). LNM was reported in 28.3% of cases with a tumor infiltration >4.25 mm (13/46), compared to 5.1% in cases with an infiltration ≤4.25 mm (2/39) (p = 0.012). In Cox regression analysis, the higher hazard ratio (HR) was reported for the LVI + and infiltration >4.25 mm (HR 24.849).

CONCLUSIONS

In patients with ECC (pT1NXM0), good differentiation (G1), absence of lymphovascular invasion (LVI-), and tumor radial infiltration ≤4.25 mm may allow performing local resection and avoiding radical surgery.

Recyclable Endogenous H2 S Activation of Self-Assembled Nanoprobe with Controllable Biodegradation for Synergistically Enhanced Colon Cancer-Specific Therapy

Excessive production of hydrogen sulfide (H₂ S) plays a crucial role in the progress of colon cancer. Construction of tumor-specific H₂ S-activated smart nanoplatform with controllable biodegradation is of great significance for precise and sustainable treatment of colon cancer. Herein, an endogenous H₂ S triggered Co-doped polyoxometalate (POM-Co) cluster with self-adjustable size, controlled biodegradation, and sustainable cyclic depletion of H₂ S/glutathione (GSH) is designed for synergistic enhanced tumor-specific photothermal and chemodynamic therapy. The designed POM-Co nanocluster holds H₂ S responsive "turn-on" photothermal property in colon cancer via self-assembling to form large-sized POM-CoS, enhancing the accumulation at tumor sites. Furthermore, the formed POM-CoS can gradually biodegrade, resulting in release of Co²⁺ and Mo⁶⁺ for Co(II)-catalyzed •OH production and Russell mechanism-enabled ¹ O₂ generation with GSH consumption, respectively. More importantly, the degraded POM-CoS is reactivated by endogenous H₂ S for recyclable and sustainable consumption of H₂ S and GSH, resulting in tumor-specific photothermal/chemodynamic continuous therapy. Therefore, this study provides an opportunity of designing tumor microenvironment-driven nanoprobes with controllable biodegradation for precise and sustainable anti-tumor therapy.

Development and validation of tumor-size-stratified prognostic nomograms for patients with uterine sarcoma: A SEER database analysis

BACKGROUND

Tumor-size-stratified analysis on the prognosis of uterine sarcoma is insufficient. This study aimed to establish the tumor-size-stratified nomograms to predict the 3- and 5-year overall survival (OS) of patients with uterine sarcoma.

METHODS

The data analyzed in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. We collected data from patients with uterine sarcoma diagnosed between 2004 and 2015. According to the median tumor size of 7.8 cm, the enrolled patients were divided into two tumor size (TS) groups: TS <7.8 cm and TS ≥7.8 cm. Patients in each group were randomly divided into the training and validation cohorts with a ratio of 7:3. Chi-square test was used to compare differences between categorical variables. Multivariate Cox regression models were used to identify significant predictors. We calculated the concordance index (C-index) and the area under the receiver operating characteristics curve (AUC) to validate the nomograms.

RESULTS

Compared with TS <7.8 cm group, TS ≥7.8 cm group had more patients of 45-64 years group, higher black race prevalence, higher proportion of myometrium tumor, higher stage, and higher grade; In the TS <7.8 cm training cohort, six variables (age, race, marital status, tumor primary site, stage, and grade) were identified as significantly associated with OS in multivariate analysis. However in the TS ≥7.8 cm training cohort, only four variables (surgery on primary site, tumor size, stage, and grade) were significantly identified; The C-index of two nomograms were 0.80 and 0.73 in training cohorts, respectively, and the AUC values for 3- and 5-year OS predictions in training cohorts were all above 0.80. Similar results were observed in validation cohorts.

CONCLUSIONS

This study found that the significant prognostic factors were different between two tumor size groups of uterine sarcoma patients. The tumor-size-stratified nomograms, which we constructed and validated, might be useful to predict the probability of survival for patients with uterine sarcoma.

No significant relationship exists between tumor size and prognosis in distant metastatic hepatocellular carcinoma: a propensity score matching analysis based on SEER database

Background

Previous studies have shown that tumor size has an impact on the prognosis of hepatocellular carcinoma (HCC). Whether tumor size is related to the prognosis of distant metastatic HCC is unclear. The purpose of this study was to investigate the effect of tumor size on the prognosis of distant metastatic HCC.

Methods

Data on patients with HCC were collected from the (SEER) database of surveillance, epidemiology and final results. Propensity score matching (PSM) was used to reduce confounding factors and comprehensively evaluate the clinicopathological features and prognosis of distant metastatic HCC.

Results

There were 189 patients with distant metastatic HCC whose tumor size was ≤ 50 mm and 615 patients with a tumor size > 50 mm. The tumor sizes of distant metastatic HCC patients were associated with race, grade, surgical treatment, N and AFP. The Kaplan–Meier analysis showed that the mortality rate of patients with a tumor size > 50 mm was higher than that of patients with a tumor size ≤ 50 mm (p = 0.00062). However, there were no significant differences in mortality rates after adjusting for confounding variables by using propensity score matching (p = 0.23).

Conclusion

This propensity score matching study provides the best data in support of the following assertions: tumor size is not an independent prognostic factor for distant metastatic HCC.

Immunohistochemical Expression of Stanniocalcin 2 in Colorectal Cancer: A Retrospective Egyptian Study

BACKGROUND & OBJECTIVE

Colorectal cancer is the third most common cause of cancer death worldwide. Stanniocalcin 2 (STC2) is a glycoprotein hormone over-expressed in many human cancers where it regulates tumor progression and invasion. Evaluating its expression in colorectal cancer and its relation with different clinicopathological parameters can provide valuable information about its role in colorectal cancer progression and behavior.

METHODS

This retrospective study was conducted on tissue samples of colorectal cancer. The STC2 immunohistochemical expression was detected and evaluated in 60 cases of colorectal cancer tissue samples of formalin-fixed and paraffin-embedded blocks. Then statistical analysis was performed to assess the relationship between its expression level and several clinicopathological parameters in the studied cases.

RESULTS

Statistically significant associations were found between the high level of STC2 immunohistochemical expression and histological tumor grade (P<0.001), tumor depth of invasion (T stage) (P=0.004), lymph node metastasis (N stage) (P=0.001), tumor Dukes' stage (P<0.001), the presence of lymphovascular invasion (P<0.001), and perineural invasion (P<0.001).

CONCLUSION

STC2 over-expression in colorectal cancer may be associated with more aggressive tumor behavior including increased tumor invasion, higher histological grade, higher rate of nodal metastasis and increased incidence of lymphovascular and perineural invasions. These data suggest a potential role for STC2 as a predictive biomarker for tumor behavior in colorectal cancer patients.

Predictive and Prognostic Effects of Primary Tumor Size on Colorectal Cancer Survival

Background

Pathologic staging is crucial in colorectal cancer (CRC). Unlike the majority of solid tumors, the current staging model does not use tumor size as a criterion. We evaluated the predictive and prognostic impact of primary tumor size on all stages of CRC.

Methods

Using the National Cancer Database (NCDB), we conducted an analysis of CRC patients diagnosed between 2010 and 2015 who underwent resection of their primary cancer. Univariate and multivariate analyses were used to identify predictive and prognostic factors, Kaplan-Meier analysis and Cox proportional hazards models for association between tumor size and survival.

Results

About 61,000 patients met the inclusion criteria. Median age was 63 years and majority of the tumors were colon primary (82.7%). AJCC stage distribution was: I - 20.1%; II - 32.1%; III - 34.7% and IV - 13.1%. The prognostic impact of tumor size was strongly associated with survival in stage III disease. Compared to patients with tumors <2cm; those with 2-5cm (HR 1.33; 1.19-1.49; p<0.001), 5-10cm (HR 1.51 (1.34-1.70; p<0.001) and >10cm (HR 1.95 (1.65-2.31; p<0.001) had worse survival independent of other variables. Stage II treated without adjuvant chemotherapy had comparable survival outcomes (HR 1.09; 0.97-1.523; p=0.148) with stage III patients who did, while Stage II patients who received adjuvant chemotherapy did much better than both groups (HR 0.76; 0.67-0.86; p<0.001). Stage III patients who did not receive adjuvant chemotherapy had the worst outcomes among the non-metastatic disease subgroups (HR 2.66; 2.48-2.86; p<0.001). Larger tumors were associated with advanced stage, MSI high, non-rectal primary and positive resection margins.

Conclusions

Further studies are needed to clarify the role of tumor size in prognostic staging models, and how to incorporate it into therapy decisions.

Predictors of Lymph Node Metastasis in T1 Colorectal Cancer in Young Patients: Results from a National Cancer Registry

The objective of this study is to fill the knowledge gap by examining predictors of lymph node metastasis (LNM) in young patients, less than 45 years, using a national cancer registry. Methods: Patients diagnosed with T1 colorectal cancer were identified in the Surveillance, Epidemiology, and End Results registry. In total, 692 patients with T1 colorectal cancer were identified. Most tumors occurred in white race (77.7%), between 40 and 44 years of age (49.4%), with grade III tumor differentiation (59.8%) and 1 to 1.9 cm size (32.2%), and were left-sided tumors (61.1%). The overall rate of LNM was 22.5% (n = 149). LNM was associated with tumor grade IV (undifferentiated) (odds ratio (OR) 2.94, CI: 1.06–8.12; p = 0.038), and increasing tumor size (1 cm–1.9 cm: OR 2.92, CI: 1.71–4.97, p < 0.001; 2.0 cm–2.9 cm: OR 2.00, CI: 1.05–3.77, p = 0.034; and ≥3.0 cm: OR 2.68, CI: 1.43–5.01, p = 0.002). Five-year cancer-specific survival for patients with LNM was 91% and for patients without LNM this was 98%. Adjusted cox proportion models showed that LNM was associated with a four times higher rate of mortality (hazard ratio (HR) 4.43, CI: 1.27–15.52, p = 0.020). In this population-based analysis of patients with T1 colorectal cancer, tumor size and grade were significant predictors of LNM.

Influence of Old Age on Risk of Lymph Node Metastasis and Survival in Patients With T1 Colorectal Cancer: A Population-Based Analysis

Background

We aimed at determining the influence of old age on lymph node metastasis (LNM) and prognosis in T1 colorectal cancer (CRC).

Methods

We collected data from eligible patients in Surveillance, Epidemiology, and End Results database between 2004 and 2015. Independent predictors of LNM were identified by logistic regression analysis. Cox regression analysis, propensity score-matched analysis, and competing risks analysis were used to analyze the associations between old age and lymph node (LN) status and to validate the prognostic value of old age on cancer-specific survival (CSS).

Results

In total, 10,092 patients were identified. Among them, 6,423 patients (63.6%) had greater than or equal to 12 examined lymph nodes (LNE ≥12), and 5,777 patients (57.7%) were 65 years or older. The observed rate of LNM was 4.6% (15 out of 325) in T1 CRC elderly patients, with tumor size <3 cm, well differentiated, with negative carcinoembryonic antigen (CEA) level, and adenocarcinoma. Logistic regression models demonstrated that tumor size ≥3 cm (odds ratio, OR = 1.316, P = 0.038), poorly differentiated (OR = 3.716, P < 0.001), older age (OR = 0.633 for ages 65–79 years, OR = 0.477 for age over 80 years, both P <0.001), and negative CEA level (OR = 0.71, P = 0.007) were independent prognostic factors. Cox regression analysis demonstrated that CSS was not significantly different between elderly patients undergoing radical resection with LNE ≥12 and those with LNE <12 (hazard ratio = 0.865, P = 0.153), which was firmly validated after a propensity score-matched analysis by a competing risks model.

Conclusions

The predictive value of tumor size, grading, primary site, histology, CEA level, and age for LNM should be considered in medical decision making about local resection. We found that tumor size was <3 cm, well differentiated, negative CEA level, and adenocarcinoma in elderly patients with T1 colorectal cancer which was suitable for local excision.

Prognostic value of modified Lauren classification in gastric cancer

BACKGROUND

It remains controversial as to which pathological classification is most valuable in predicting the overall survival (OS) of patients with gastric cancer (GC).

AIM

To assess the prognostic performances of three pathological classifications in GC and develop a novel prognostic nomogram for individually predicting OS.

METHODS

Patients were identified from the Surveillance, Epidemiology, and End Results program. Univariate and multivariate analyses were performed to identify the independent prognostic factors. Model discrimination and model fitting were evaluated by receiver operating characteristic curves and Akaike information criteria. Decision curve analysis was performed to assess clinical usefulness. The independent prognostic factors identified by multivariate analysis were further applied to develop a novel prognostic nomogram.

RESULTS

A total of 2718 eligible GC patients were identified. The modified Lauren classification was identified as one of the independent prognostic factors for OS. It showed superior model discriminative ability and model-fitting performance over the other pathological classifications, and similar results were obtained in various patient settings. In addition, it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3- and 5-year OS. A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability, model-fitting performance, and net benefits over the American Joint Committee on Cancer 8^th edition tumor-node-metastasis classification.

CONCLUSION

The modified Lauren classification shows superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS. A novel prognostic nomogram incorporating the modified Lauren classification shows good model discriminative ability, model-fitting performance, and net benefits.

Prognostic impact of tumor length in esophageal Cancer: a systematic review and Meta-analysis

BACKGROUND

In clinical studies, it has been observed that esophageal cancer (EC) patient prognosis can be very different even for those patients with tumors of the same TNM stage. Tumor length has been analysed as a possible independent prognostic factor in many studies, but no unanimous conclusion has been reached. Therefore, this review used a meta-analysis to evaluate the association between tumor length and prognosis in EC patients.

METHODS

A systematic search for relevant articles was performed in PubMed, Web of Science, and Embase. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used as effective measures to estimate the correlation between tumor length and prognosis, including overall survival, disease-free survival, progression-free survival, disease-specific survival, and cancer-specific survival. STATA 15.0 software was used to perform the meta-analysis and the data synthesis.

RESULTS

Finally, 41 articles with 28,973 patients were included in our study. The comprehensive statistical results showed that long tumors are an independent prognostic parameter associated with poor overall survival (OS) (HR = 1.30; 95% CI: 1.21-1.40, p < .001) and disease-free survival (DFS) (HR = 1.38; 95% CI: 1.18-1.61, p < .001) in EC patients. Subgroup analyses also suggested a significant correlation between long tumors and poor OS. Sensitivity analysis and publication bias evaluation confirmed the reliability and stability of the results. Similar results were obtained in the analyses of progression-free survival (PFS), disease-specific survival (DSS), and cancer-specific survival (CSS).

CONCLUSION

The results of this meta-analysis showed that long tumors were related to poor OS, DFS, PFS, DSS and CSS in EC patients. Tumor length might be an important predictor of prognosis in EC patients, and it can be used as an independent staging index. Further well-designed and large-scale prospective clinical studies are needed to confirm these findings.

Nomograms to predict cancer-specific mortality in colon adenocarcinoma with different types of villous architecture

PURPOSE

The role of villous architecture in the prognosis of colon adenocarcinoma remains unclear. This study aimed to investigate the prognostic factors of colon adenocarcinoma with different types of villous architecture and to establish nomograms for predicting cancer-specific mortality.

METHODS

This retrospective study included 10,427 patients with colon adenocarcinoma arising in adenomas with villous architectures. The patients were stratified into the tubulovillous adenocarcinoma cohort and villous adenocarcinoma cohort. The prognostic risk factors, which were incorporated into nomograms for survival prediction, were determined by the log-rank test and Cox hazard models. The Harrell's Concordance Index (C-index) and calibration curve were utilized to evaluate the prediction accuracy.

RESULTS

The pathological type of villous architecture was independently associated with the mortality of the entire population. Age, race, tumor size, T/N/M stage, and chemotherapy were independent risk factors of mortality in both cohorts. Interestingly, tumor differentiation was a prognostic factor for tubulovillous adenocarcinoma rather than villous adenocarcinoma, while the retrieved lymph node number was a prognostic factor for villous adenocarcinoma rather than tubulovillous adenocarcinoma. Survival analysis showed that the mortality rate of villous adenocarcinoma was higher than that of tubulovillous adenocarcinoma (HR 1.361, P < 0.001). We then established nomograms to predict the mortality of both cohorts and found excellent discrimination and predictive accuracy (C-index 0.842 and 0.821).

CONCLUSION

Villous architecture is a determinant of colon adenocarcinoma outcomes, which might prompt reports of villous architecture in colon adenocarcinoma specimens by pathologists. Our population-based nomograms could be useful for predicting the survival of patients with colon adenocarcinoma and guiding individualized treatments.

Tumor size improves the accuracy of the prognostic prediction of T4a stage colon cancer

The aim of this study was to evaluate the potential impact of tumor size on the long-term outcome of colon cancer (CC) patients after curative surgery. A total of 782 curatively resected T4a stage CC patients without distant metastasis were enrolled. Patients were categorized into 2 groups according to the best threshold of tumor size: larger group (LG) and smaller group (SG). Propensity score matching was used to adjust for the differences in baseline characteristics. The ideal cutoff point of tumor size was 5 cm. In the multivariate analysis for the whole study series, tumor size was an independent prognostic factor. Patients in the LG had significant lower 5-year overall survival (OS) and relapse-free survival (RFS) rates (OS: 63.5% versus 75.2%, P < 0.001; RFS: 59.5% versus 72.4%, P < 0.001) than those in the SG. After matching, patients in the LG still demonstrated significant lower 5-year OS and RFS rates than those in the SG. The modified tumor-size-node-metastasis (mTSNM) staging system including tumor size was found to be more appropriate for predicting the OS and RFS of T4a stage CC than TNM stage, and the -2log likelihood of the mTSNM staging system was smaller than the value of TNM stage. In conclusion, tumor size was an independent prognostic factor for OS and RFS. We maintain that tumor size should be incorporated into the staging system to enhance the accuracy of the prognostic prediction of T4a stage CC patients.

Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer

Purpose

Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.

Materials and Methods

A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.

Results

Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).

Conclusion

ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

Prognostic nomogram to predict the overall survival of patients with early-onset colorectal cancer: a population-based analysis

PURPOSE

The present study aimed to identify independent clinicopathological and socio-economic prognostic factors associated with overall survival of early-onset colorectal cancer (EO-CRC) patients and then establish and validate a prognostic nomogram for patients with EO-CRC.

METHODS

Eligible patients with EO-CRC diagnosed from 2010 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training cohort and a testing cohort. Independent prognostic factors were obtained using univariate and multivariate Cox analyses and were used to establish a nomogram for predicting 3- and 5-year overall survival (OS). The discriminative ability and calibration of the nomogram were assessed using C-index values, AUC values, and calibration plots.

RESULTS

In total, 5585 patients with EO-CRC were involved in the study. Based on the univariate and multivariate analyses, 15 independent prognostic factors were assembled into the nomogram to predict 3- and 5-year OS. The nomogram showed favorable discriminatory ability as indicated by the C-index (0.840, 95% CI 0.827-0.850), and the 3- and 5-year AUC values (0.868 and 0.84869 respectively). Calibration plots indicated optimal agreement between the nomogram-predicted survival and the actual observed survival. The results remained reproducible in the testing cohort. The C-index of the nomogram was higher than that of the TNM staging system (0.840 vs 0.804, P < 0.001).

CONCLUSION

A novel prognostic nomogram for EO-CRC patients based on independent clinicopathological and socio-economic factors was developed, which was superior to the TNM staging system. The nomogram could facilitate postoperative individual prognosis prediction and clinical decision-making.

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

PURPOSE

The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely studied.

MATERIALS AND METHODS

Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.

RESULTS

LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.

CONCLUSION

Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.

Association of tumor size with prognosis in colon cancer: A Surveillance, Epidemiology, and End Results (SEER) database analysis

BACKGROUND

Thus far, the association of tumor size with prognosis in colon cancer has not been considered and has remained unclear. This study, therefore, aimed to investigate the association between tumor size as a continuous variable and prognosis in colon cancer using Cox models with restricted cubic splines.

METHODS

Using the Surveillance, Epidemiology, and End Results database, we selected 128,369 patients with colon cancer who underwent surgery. Overall survival and colon cancer-specific survival were separately analyzed, and tumor size was separately evaluated as a continuous variable and a categorical variable. To investigate the relationship after adjusting for covariates, we used the proportional hazards models. The restricted cubic splines model was used to determine the presence of nonlinear or linear association and flexibly visualize the association.

RESULTS

The adjusted covariate model showed that the hazard ratio of colon cancer rapidly increased with a tumor size of 4 cm and slowly increased with a tumor size larger than 4 cm. When tumor size was analyzed as a categorical variable, the multivariable-adjusted model demonstrated a nearly linear relationship between tumor size and hazard ratio regardless of overall survival or cancer-specific survival, and the hazard ratio of group 5 (4.1-5 cm) was nearly a turning point. Subgroup analysis with respect to lymph node metastasis showed that the relationship between tumor size and prognosis in colon cancer was evident in lymph node metastasis.

CONCLUSION

There was a strong negative relationship between tumor size and prognosis in colon cancer. However, when tumor size was less than 4 cm, the relationship between tumor size and prognosis was steep compared with that when tumor size was larger than 4 cm, especially in lymph node metastasis.

Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Patients With Early Onset Colon Adenocarcinoma

Background

The incidence of colon cancer in young patients is on the rise, of which adenocarcinoma is the most common pathological type. However, a reliable nomogram for early onset colon adenocarcinoma (EOCA) to predict prognosis is currently lacking. This study aims to develop nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with EOCA.

Methods

Patients diagnosed with EOCA from 2010 to 2015 were included and randomly assigned to training set and validation set. Cox regression models were used to evaluate prognosis and identify independent predictive factors, which were then utilized to establish the nomograms for predicting 3- and 5-year OS and CSS. The discrimination and calibration of nomograms were validated using the calibration plots, concordance index, receiver operating characteristics curve, and the decision curve analysis.

Results

A total of 2,348 patients were screened out, with 1,644 categorized into the training set and 704 into the validation set. Multivariate analysis demonstrated that gender, age, tumor size, T stage, M stage, regional node, tumor deposits, lung metastasis and perineural invasion were significantly correlated with OS and CSS. The calibration plots indicated that there was good consistency between the nomogram prediction and actual observation. The C-indices for training set of OS and CSS prediction nomograms were 0.735 (95% CI: 0.708-0.762) and 0.765 (95% CI: 0.739-0.791), respectively, whereas those for validation set were 0.736 (95% CI: 0.696-0.776) and 0.76 (95% CI: 0.722-0.798), respectively. The results of ROC analysis revealed the nomograms showed a good discriminate power. The 3- and 5-year DCA curves displayed superiority over TNM staging system with higher net benefit gains.

Conclusions

The nomograms established could effectively predict 3- and 5-year OS and CSS in EOCA patients, which assisted clinicians to evaluate prognosis more accurately and optimize treatment strategies.