A new nomogram for recurrence-free survival prediction of gastrointestinal stromal tumors: Comparison with current risk classification methods

Pathologic diagnosis and molecular features of gastrointestinal stromal tumors: a mini-review

Gastrointestinal stromal tumors (GISTs) arise from the interstitial cells of Cajal, predominantly affecting the stomach and small intestine, with rare occurrences in the duodenum, rectum, and extraintestinal sites. Histologically, GISTs can present as spindle cells, epithelioid cells, or mixed morphologies, with immunohistochemical staining revealing expression of KIT (CD117) and discovered on GIST 1 (DOG1). Approximately 80% of GISTs harbor activating mutations in KIT or platelet derived growth factor receptor α (PDGFRA), which influence their clinical behavior and treatment response. SDH-deficient GISTs, associated with syndromes such as Carney triad and Carney-Stratakis syndrome, represent a distinct subgroup with unique characteristics and management challenges. The standard treatment includes surgery and imatinib for metastatic cases; however, resistance to tyrosine kinase inhibitors remains a significant hurdle, especially in pediatric and wildtype GISTs. This highlights the need for advanced therapeutic strategies and emphasizes the importance of molecular profiling in guiding treatment decisions and improving outcomes for GIST patients.

Establishment of nomogram to predict overall survival and cancer-specific survival of local tumor resection in patients with colorectal cancer liver metastasis with unresectable metastases: a large population-based analysis

BACKGROUND AND PURPOSE

The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients.

METHODS

Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical.

RESULTS

3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan-Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant.

CONCLUSIONS

The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND

Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes.

AIM

To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment.

METHODS

Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort.

RESULTS

A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit.

CONCLUSION

Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External Validation Based Study

Background

Gastric cancer (GC) has a poor prognosis, particularly in patients with liver metastasis (LM). This study aims to identify relevant factors associated with the occurrence of LM in GC patients and factors influencing the prognosis of gastric cancer with liver metastasis (GCLM) patients, in addition to developing diagnostic and prognostic nomograms specifically.

Patients and Methods

Overall, 6184 training data were from the Surveillance, Epidemiology, and End Results (SEER) database from 2011 to 2015. 1527 validation data were from our hospital between January 2018 and December 2022. Logistic regression was used to identify the risk factors associated with the occurrence of LM in GC patients, Cox regression was used to confirm the prognostic factors of GCLM patients. Two nomogram models were established to predict the risk and overall survival (OS) of patients with GCLM. The performance of the two models was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves.

Results

A nomogram included five independent factors from multivariate logistic regression: sex, lymph node removal, chemotherapy, T stage and N stage were constructed to calculate the possibility of LM. Internal and external verifications of AUC were 0.786 and 0.885, respectively. The other nomogram included four independent factors from multivariate Cox regression: surgery at primary site, surgery at other site, chemotherapy, and N stage were constructed to predict OS. C-index for internal and external validations were 0.714 and 0.702, respectively, and the calibration curves demonstrated the robust discriminative ability of the models.

Conclusion

Based on the SEER database and validation data, we defined effective nomogram models to predict risk and OS in patients with GCLM. They have important value in clinical decision-making and personalized treatment.

Raf kinase inhibitor protein combined with phosphorylated extracellular signal-regulated kinase offers valuable prognosis in gastrointestinal stromal tumor

BACKGROUND

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Tyrosine kinase inhibitors, such as imatinib, have been used as first-line therapy for the treatment of GISTs. Although these drugs have achieved considerable efficacy in some patients, reports of resistance and recurrence have emerged. Extracellular signal-regulated kinase 1/2 (ERK1/2) protein, as a member of the mitogen-activated protein kinase (MAPK) family, is a core molecule of this signaling pathway. Nowadays, research reports on the important clinical and prognostic value of phosphorylated-ERK (P-ERK) and phosphorylated-MAPK/ERK kinase (P-MEK) proteins closely related to raf kinase inhibitor protein (RKIP) have gradually emerged in digestive tract tumors such as gastric cancer, colon cancer, and pancreatic cancer. However, literature on the expression of these downstream proteins combined with RKIP in GIST is scarce. This study will focus on this aspect and search for answers to the problem.

AIM

To detect the expression of RKIP, P-ERK, and P-MEK protein in GIST and to analyze their relationship with clinicopathological characteristics and prognosis of this disease. Try to establish a new prognosis evaluation model using RKIP and P-ERK in combination with analysis and its prognosis evaluation efficacy.

METHODS

The research object of our experiment was 66 pathologically diagnosed GIST patients with complete clinical and follow-up information. These patients received surgical treatment at China Medical University Affiliated Hospital from January 2015 to January 2020. Immunohistochemical method was used to detect the expression of RKIP, P-ERK, and P-MEK proteins in GIST tissue samples from these patients. Kaplan-Meier method was used to calculate the survival rate of 63 patients with complete follow-up data. A Nomogram was used to represent the new prognostic evaluation model. The Cox multivariate regression analysis was conducted separately for each set of risk evaluation factors, based on two risk classification systems [the new risk grade model vs the modified National Institutes of Health (NIH) 2008 risk classification system]. Receiver operating characteristic (ROC) curves were used for evaluating the accuracy and efficiency of the two prognostic evaluation systems.

RESULTS

In GIST tissues, RKIP protein showed positive expression in the cytoplasm and cell membrane, appearing as brownish-yellow or brown granules. The expression of RKIP was related to GIST tumor size, NIH grade, and mucosal invasion. P-ERK protein exhibited heterogeneous distribution in GIST cells, mainly in the cytoplasm, with occasional presence in the nucleus, and appeared as brownish-yellow granules, and the expression of P-ERK protein was associated with GIST tumor size, mitotic count, mucosal invasion, and NIH grade. Meanwhile, RKIP protein expression was negatively correlated with P-ERK expression. The results in COX multivariate regression analysis showed that RKIP protein expression was not an independent risk factor for tumor prognosis. However, RKIP combined with P-ERK protein expression were identified as independent risk factors for prognosis with statistical significance. Furthermore, we establish a new prognosis evaluation model using RKIP and P-ERK in combination and obtained the nomogram of the new prognosis evaluation model. ROC curve analysis also showed that the new evaluation model had better prognostic performance than the modified NIH 2008 risk classification system.

CONCLUSION

Our experimental results showed that the expression of RKIP and P-ERK proteins in GIST was associated with tumor size, NIH 2008 staging, and tumor invasion, and P-ERK expression was also related to mitotic count. The expression of the two proteins had a certain negative correlation. The combined expression of RKIP and P-ERK proteins can serve as an independent risk factor for predicting the prognosis of GIST patients. The new risk assessment model incorporating RKIP and P-ERK has superior evaluation efficacy and is worth further practical application to validate.

Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features

OBJECTIVE

Development and validation of a radiomics nomogram for predicting recurrence and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal tumors (GISTs) based on computed tomography (CT) radiomic features.

METHODS

Retrospectively collected from 2009.07 to 2015.09, 220 patients with pathological diagnosis of intermediate- and high-risk stratified gastrointestinal stromal tumors and received imatinib treatment were randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest (ROI) was delineated from the portal-phase images on contrast-enhanced (CE) CT, and radiological features were extracted. The most valuable radiological features were obtained using a Lasso-Cox regression model. Integrated construction was conducted of nomograms of radiomics characteristics to predict recurrence-free survival (RFS) in patients receiving adjuvant therapy.

RESULTS

Eight radiomic signatures were finally selected. The area under the curve (AUC) of the radiomics signature model for predicting 3-, 5-, and 7-year RFS in the training and validation cohorts (training cohort AUC = 0.80, 0.84, 0.76; validation cohort AUC = 0.78, 0.80, 0.76). The constructed radiomics nomogram was more accurate than the clinicopathological nomogram for predicting RFS in GIST (C-index: 0.864 95%CI, 0.817-0.911 vs. 0.733 95%CI, 0.675-0.791). Kaplan-Meier survival curve analysis showed a greater benefit from adjuvant therapy in patients with high radiomics scores (training cohort: p < 0.0001; validation cohort: p = 0.017), while there was no significant difference in the low-score group (p > 0.05).

CONCLUSION

In this study, a nomogram constructed based on preoperative CT radiomics features could be used for RFS prediction in high/intermediate-risk GISTs and assist the clinical decision-making for GIST patients.

Postoperative Adjuvant Imatinib Therapy-Associated Nomogram to Predict Overall Survival of Gastrointestinal Stromal Tumor

BACKGROUND

Adjuvant imatinib therapy has been shown to improve overall survival (OS) of gastrointestinal stromal tumor (GIST) significantly. Few nomograms combining the use of adjuvant imatinib and clinicopathological characteristics estimate the outcome of patients. We aimed to establish a more comprehensive nomogram for predicting OS in patients with GIST.

METHODS

In total, 1310 GIST patients undergoing curative resection at four high-volume medical centers between 2001 and 2015 were enrolled. Independent prognostic factors were identified by multivariate Cox analysis. Eligible patients were randomly assigned in a ratio of 7:3 into a training set (916 cases) and a validation set (394 cases). A nomogram was established by R software and its predictive power compared with that of the modified National Institutes of Health (NIH) classification using time-dependent receiver operating characteristic (ROC) curves and calibration plot.

RESULTS

Age, tumor site, tumor size, mitotic index, postoperative imatinib and diagnostic delay were identified as independent prognostic parameters and used to construct a nomogram. Of note, diagnostic delay was for the first time included in a prognostic model for GIST. The calibrated nomogram resulted in predicted survival rates consistent with observed ones. And the decision curve analysis suggested that the nomogram prognostic model was clinically useful. Furthermore, time-dependent ROC curves showed the nomogram exhibited greater discrimination power than the modified NIH classification in 3- and 5-year survival predictions for both training and validation sets (all P < 0.05).

CONCLUSIONS

Postoperative adjuvant imatinib therapy improved the survival of GIST patients. We developed and validated a more comprehensive prognostic nomogram for GIST patients, and it could have important clinical utility in improving individualized predictions of survival risks and treatment decision-making.

Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

Assessment of Systemic Inflammation and Nutritional Indicators in Predicting Recurrence-Free Survival After Surgical Resection of Gastrointestinal Stromal Tumors

Background

Recent studies have shown that the systemic inflammation and nutritional indicators are prognostic for a variety of malignancies. However, only limited data have so far demonstrated their usefulness in gastrointestinal mesenchymal tumors (GIST).

Methods

We retrospectively analyzed the data of GIST patients who underwent radical surgery in Beijing hospital from October 2004 to July 2018. The area under the receiver operating characteristic curve (AUC) was used to compare several commonly used inflammatory and nutritional indicators. The indicators with largest AUC were further analysis. Optimal cut-off values of those indicators in predicting recurrence-free survival (RFS) were determined. Kaplan-Meier curve and the time-dependent receiver operating characteristic (ROC) curve were used to assess the prognostic values. We then used univariate and multivariate Cox regression analyses to identify prognostic factors that were associated with RFS.

Results

In total, 160 patients who underwent surgery for GIST were included in the study. The median survival time was 34.5 months, with 1-, 3-, and 5-year RFS rates of 96.1%, 84.7%, and 80.8%, respectively. The inflammatory and nutritional indicators with largest AUC were Systemic immunoinflammatory Index (SII) and Geriatric Nutrition Risk Index (GNRI), reached 0.650 and 0.713, respectively. The optimal cutoff of GNRI and SII were 98.3, and 820.0, respectively. Univariate analysis showed that GNRI, SII, KI67, surgery method, tumor location, tumor size, and mitotic index were all significant prognostic indicators of RFS. After multivariate Cox analysis, independent prognostic factors for RFS in GIST included tumor location, mitotic index, tumor size, and GNRI (HR=2.802,95% CI: 1.045 to 7.515, p = 0.041). Besides, SII also tended to be associated with RFS (HR = 2.970, 95% CI: 0.946 to 9.326, p = 0.062).

Conclusions

High GNRI is an independent prognostic factor for RFS in GIST, while SII can be considered as a prognostic factor. GNRI and SII can be used as tools to evaluate the prognosis of patients before surgery, helping doctors to better treat high-risk patients.

Novel Prognostic Nomogram for Recurrence-Free Survival of Patients With Primary Gastrointestinal Stromal Tumors After Surgical Resection: Combination of Prognostic Nutritional Index and Basic Variables

BACKGROUND

Gastrointestinal stromal tumor (GIST) is the most common type of mesenchymal tumors in the digestive tract, often recrudescing even after R0 resection. Adjuvant tyrosine kinase inhibitor therapy prolonged recurrence-free survival (RFS). This study aimed to develop a novel nomogram for predicting the RFS of patients following surgical resection of GISTs.

METHODS

Clinicopathologic data of patients with GISTs at Tianjin Medical University General Hospital (Tianjin, China) from January 2000 to October 2019 were retrospectively reviewed. Univariate and multivariate Cox regression analyses were used to select the suitable variables from the training cohort to construct a nomogram for 2- and 5-year RFS. The 1,000 bootstrap samples and calibration curves were used to validate the discrimination of the nomogram. The receiver operating characteristic analysis(ROC) was used to compare the predictive ability of the nomogram and present four commonly used risk stratification systems: National Institutes of Health (NIH)-Fletcher staging system; NIH-Miettinen criteria; Modified NIH criteria; and Air Forces Institute of Pathology risk criteria (AFIP).

RESULTS

Univariate and multivariate analyses showed that the tumor site, tumor size, mitotic index, tumor rupture, and prognostic nutritional index were significant factors associated with RFS. These variables were selected to create the nomogram for 2- and 5-year RFS (all P<0.05). The 2- and 5-year the ROC of the nomogram were 0.821 (95% confidence interval [CI]: 0.740-0.903) and 0.798 (95% CI: 0.739-0.903); NIH-Fletcher criteria were 0.757 (95% CI: 0.667-0.846) and 0.683 (95% CI: 0.613-0.753); NIH-Miettinen criteria were 0.762 (95% CI: 0.678-0.845) and 0.718 (95% CI: 0.653-0.783); Modified NIH criteria were 0.750 (95% CI: 0.661-0.838) and 0.689 (95% CI: 0.619-0.760); and AFIP were 0.777 (95% CI: 0.685-0.869) and 0.708 (95% CI: 0.636-0.780). Hence, the predictive probabilities of our nomogram are better than those of other GIST risk stratification systems.

CONCLUSION

This nomogram, combining tumor site, tumor size, mitotic index, tumor rupture, and prognostic nutritional index, may assist physicians in providing individualized treatment and surveillance protocols for patients with GISTs following surgical resection.

Fibrinogen/Albumin Ratio Index Is an Independent Prognosis Predictor of Recurrence-Free Survival in Patients After Surgical Resection of Gastrointestinal Stromal Tumors

Background: Nutritional status, systemic inflammation, and coagulation mechanism are closely related to tumor progression. Herein, we examined the role of fibrinogen-to-albumin ratio index (FARI) in the prognosis of gastrointestinal stromal tumors (GISTs) and developed a novel nomogram predicting recurrence-free survival (RFS).

Methods: We retrospectively analyzed data from 357 GIST patients admitted at the gastrointestinal surgery of the Beijing Hospital from January 2008 to January 2018 and underwent curative resection. FARI was calculated as fibrinogen level (g/L) /albumin level (g/L). The cutoff point of FARI was set using the point with the largest Youden index on the receiver operating characteristic curve with the 5-years recurrence-free survival as an endpoint. We used the Kaplan-Meier approach and multivariable Cox regression model to study the impact of FARI on recurrence-free survival. Finally, we developed a nomogram based on tumor size, location, mitotic index, and FARI to predict RFS. The nomogram was assessed by calculating concordance probabilities and testing calibration of predicted RFS with observed RFS. Concordance probabilities were also compared with the National Institute of Health (NIH) risk classification system.

Results: The ROC curve revealed that the best cutoff point of the FARI was set as 0.08. The patients were classified into the FARI-high (≥0.08) and FARI-low (<0.08) groups. FARI was significantly associated with age, size of the tumor, NIH risk category, and Mitotic Index (all P < 0.05). FARI was weakly associated with NLR and PLR. FARI and PNI had a weak negative association. Multivariate analysis showed that the NIH risk category and FARI were independent prognostic predictors for worse outcomes concerning RFS in GIST patients. In the high-risk subgroup, patients with low FARI also had a more prolonged RFS than patients with high FARI (P < 0.05). The nomogram had a concordance probability of 0.802 (SE 0.025). Nomogram predictions were well-calibrated. Concordance probabilities of the nomogram were better than NIH risk classification system [0.802 [0.025] vs. 0.737 [0.024], p < 0.01].

Conclusion: We established that preoperative FARI is a novel serum biomarker to predict the prognosis after surgical resection of GISTs. The nomogram incorporating FARI could be used to help the decision-making of clinical treatment.