Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR)

OBJECTIVE

To assess the effectiveness of a machine learning framework and nomogram in predicting progression-free survival (PFS) post-radical gastrectomy in patients with dMMR.

METHOD

Machine learning models and nomograms to forecast PFS in patients undergoing radical gastrectomy for nonmetastatic gastric cancer with dMMR. Independent risk factors were identified using Cox regression analysis to develop the nomogram. The performance of the models was assessed through C-index, time receiver operating characteristic (T-ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram's risk scores.

RESULTS

Among the 582 patients studied, machine learning models exhibited higher c-index values than the nomogram. Random Survival Forests (RSF) demonstrated the highest c-index (0.968), followed by Extreme Gradient Boosting (XG boosting, 0.945), Decision Survival Tree (DST, 0.924), the nomogram (0.808), and 8th TNM staging (0.757). All models showed good calibration with low integrated Brier scores (< 0.1), although there was calibration drift over time, particularly in the traditional nomogram model. DCA showed an incremental net benefit from all machine learning models compared with conventional models currently used in practice. Age, positive lymph nodes, neural invasion, and Ki67 were identified as key factors and integrated into the prognostic nomogram.

CONCLUSION

Our research has demonstrated the effectiveness of the RSF algorithm in accurately predicting progression-free survival (PFS) in dMMR gastric cancer patients after gastrectomy. The nomogram created from this algorithm has proven to be a valuable tool in identifying high-risk patients, providing clinicians with important information for postoperative monitoring and personalized treatment strategies.

Gender impact on pancreatic neuroendocrine neoplasm (PanNEN) prognosis according to survival nomograms

PURPOSE

Personalizing care and outcome evaluation are important aims in the field of NEN and nomograms may represent useful tools for clinicians. Of note, gender difference is being progressively more considered in NEN care, as it may also impact on survival. This systematic review aims to describe and analyze the available nomograms on pancreatic NENs (PanNENs) to identify if gender differences are evaluated and if they could impact on patients' management and prognosis.

METHODS

We performed an electronic-based search using PubMed updated until June 2024, summarizing the available evidence of gender impact on PanNEN survival outcomes as emerges from published nomograms.

RESULTS

34 articles were identified regarding prognostic nomograms in PanNEN fields. The most included variables were age, tumor grade, tumor stage, while only 5 papers (14.7%) included sex as one of the key model variables with a significant impact on patients' prognosis. These 5 studies analyzed a total of 18,920 PanNENs. 3 studies found a significant impact of sex on overall survival (OS), whereas the remaining 2 studies showed no significant impact of sex on OS.

CONCLUSIONS

Gender difference is being progressively more considered in PanNEN diagnosis, care and survival. Nomograms represent a potentially useful tool in patients' management and in outcomes prediction in the field of PanNENs. A key role of sex in the prognosis of PanNENs has been found in few models, while definitive conclusions couldn't be drawn. Future studies are needed to finally establish gender impact on PanNEN prognosis.

Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms

The GATIS score, developed by Zeng et al, represents a significant advancement in predicting the prognosis of patients with rectal neuroendocrine neoplasms (R-NENs). This study, which included 1408 patients from 17 major medical centres in China over 12 years, introduces a novel prognostic model based on the tumour grade, T stage, tumour size, age, and the prognostic nutritional index. Compared with traditional methods such as the World Health Organization classification and TNM staging systems, the GATIS score has superior predictive power for overall survival and progression-free survival. With a C-index of 0.915 in the training set and 0.812 in the external validation set, the GATIS score's robustness and reliability are evident. The study's use of a large, multi-centre cohort and rigorous validation processes underscore its significance. The GATIS score offers clinicians a powerful tool to accurately predict patient outcomes, guide treatment decisions, and improve follow-up strategies. This development represents a crucial step forwards in the management of R-NENs, addressing the complexity and variability of these tumours and setting a new benchmark for future research and clinical practice.

Variable screening and model construction for prognosis of elderly patients with lower-grade gliomas based on LASSO-Cox regression: a population-based cohort study

Background

This study aimed to identify prognostic factors for survival and develop a prognostic nomogram to predict the survival probability of elderly patients with lower-grade gliomas (LGGs).

Methods

Elderly patients with histologically confirmed LGG were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. These individuals were randomly allocated to the training and validation cohorts at a 2:1 ratio. First, Kaplan-Meier survival analysis and subgroup analysis were performed. Second, variable screening of all 13 variables and a comparison of predictive models based on full Cox regression and LASSO-Cox regression analyses were performed, and the key variables in the optimal model were selected to construct prognostic nomograms for OS and CSS. Finally, a risk stratification system and a web-based dynamic nomogram were constructed.

Results

A total of 2307 elderly patients included 1220 males and 1087 females, with a median age of 72 years and a mean age of 73.30 ± 6.22 years. Among them, 520 patients (22.5%) had Grade 2 gliomas, and 1787 (77.5%) had Grade 3 gliomas. Multivariate Cox regression analysis revealed four independent prognostic factors (age, WHO grade, surgery, and chemotherapy) that were used to construct the full Cox model. In addition, LASSO-Cox regression analysis revealed five prognostic factors (age, WHO grade, surgery, radiotherapy, and chemotherapy), and a LASSO model was constructed. A comparison of the two models revealed that the LASSO model with five variables had better predictive performance than the full Cox model with four variables. Ultimately, five key variables based on LASSO-Cox regression were utilized to develop prognostic nomograms for predicting the 1-, 2-, and 5-year OS and CSS rates. The nomograms exhibited relatively good predictive ability and clinical utility. Moreover, the risk stratification system based on the nomograms effectively divided patients into low-risk and high-risk subgroups.

Conclusion

Variable screening based on LASSO-Cox regression was used to determine the optimal prediction model in this study. Prognostic nomograms could serve as practical tools for predicting survival probabilities, categorizing these patients into different mortality risk subgroups, and developing personalized decision-making strategies for elderly patients with LGGs. Moreover, the web-based dynamic nomogram could facilitate its use in the clinic.

Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation

RATIONALE AND OBJECTIVES

We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups.

RESULTS

PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively.

CONCLUSION

Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.

A CT-based diagnostic nomogram and survival analysis for differentiating grade 3 pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas

Objective

To construct a CT-based diagnostic nomogram for distinguishing grade 3 pancreatic neuroendocrine tumors (G3 PNETs) from pancreatic ductal adenocarcinomas (PDACs) and assess their respective survival outcomes.

Methods

Patients diagnosed with G3 PNETs (n = 30) and PDACs (n = 78) through surgery or biopsy from two medical centers were retrospectively identified. Demographic and radiological information, including age, gender, tumor diameter, shape, margin, dilatation of pancreatic duct, and invasive behavior, were carefully collected. A nomogram was established after univariate and multivariate logistic regression analyses. The Kaplan-Meier survival was performed to analyze their survival outcomes.

Results

Factors with a p-value <0.05, including age, CA 19-9, pancreatic duct dilatation, irregular shape, ill-defined margin, pancreatic atrophy, combined pancreatitis, arterial/portal enhancement ratio, were included in the multivariate logistic analysis. The independent predictive factors, including age (OR, 0.91; 95% CI, 0.85-0.98), pancreatic duct dilatation (OR, 0.064; 95% CI, 0.01-0.32), and portal enhancement ratio (OR, 1,178.08; 95% CI, 5.96-232,681.2) were determined to develop a nomogram. The internal calibration curve and decision curve analysis demonstrate that the nomogram exhibits good consistency and discriminative capacity in distinguishing G3 PNETs from PDACs. Patients diagnosed with G3 PNETs exhibited considerably better overall survival outcomes compared to those diagnosed with PDACs (median survival months, 42 vs. 9 months, p < 0.001).

Conclusions

The nomogram model based on age, pancreatic duct dilatation, and portal enhancement ratio demonstrates good accuracy and discriminative ability effectively predicting the probability of G3 PNETs from PDACs. Furthermore, patients with G3 PNETs exhibit better prognosis than PDACs.

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics

BACKGROUND

Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.

METHODS

Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model's performance was validated in both internal and external test cohorts.

RESULTS

Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).

CONCLUSION

A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience

BACKGROUND

There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs).

AIM

To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort.

METHODS

Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed.

RESULTS

A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods.

CONCLUSION

The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care.