Prediction of Overall Survival and Novel Classification of Patients with Gastric Cancer Using the Survival Recurrent Network

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

A novel nomogram for predicting risk of hypertension in US adults with periodontitis: National Health and Nutrition Examination Survey (NHANES) 2009-2014

The goal of our study was to create a nomogram to predict the risk of developing hypertension in patients with periodontitis. Our study used data from a total of 3196 subjects from the National Health and Nutrition Examination Survey 2009 to 2014 who had ever been diagnosed with periodontitis. The data set was randomly divided into a training set and a validation set according to a 7:3 ratio. The data from the training set was utilized to build the prediction model, while the validation set were used to validate the model. To identify the risk variables, stepwise regression was used to perform successive univariate and multivariate logistic regression analysis. The predictive ability of the nomogram model was evaluated using receiver operating characteristic curve. Calibration plots were used to assess the consistency of the prediction model. The clinical value of the model was evaluated using decision curve analysis and clinical impact curve. A nomogram for the risk of hypertension in subjects with periodontitis was constructed in accordance with the 8 predictors identified in this study. The areas under the receiver operating characteristic curve values for the training set and validation set were 0.922 (95% confidence interval: 0.911-0.933) and 0.918 (95% confidence interval: 0.900-0.935), respectively, indicating excellent discrimination. The decision curve analysis and clinical impact curve suggested that the model has significant clinical applications, and the calibration plots of the training set and validation set demonstrated good consistency. The nomogram can effectively predict the risk of hypertension in patients with periodontitis and help clinicians make better clinical decisions.

Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer

The present study employed artificial intelligence (AI) machine learning technology to evaluate the prognosis of gastric cancer using blood collection data, commonly used in clinical practice and subsequently performed a stratification distinct from conventional tumor-node-metastasis (TNM) classification. Experiments were conducted using four machine learning methods, namely, logistic regression (LR), random forest (RF), gradient boosting (GB) and deep neural network (DNN), to classify good or poor post-5-year prognosis based on clinicopathological data and post-5-year relapse occurrence. For each machine learning method, the importance was sorted in descending order (from the most to the least); the top features were used for clustering using the k-medoids method. The prediction accuracy and area under the curve (AUC) for 5-year survival were as follows: LR, 76.8% and 0.702; RF, 72.5% and 0.721; GB, 75.3% and 0.73; DNN, 76.9% and 0.682, respectively. The prediction accuracy and AUC for 5-year recurrence-free survival were as follows: LR, 85.5% and 0.692; RF, 79.0% and 0.721; GB, 80.5% and 0.718; DNN, 83.2% and 0.670. Clustering patients into three groups resulted in a stratification distinct from the TNM classification. In conclusion, AI machine learning using routine clinical data can help evaluate the prognosis of gastric cancer, with prognosis differing according to AI-identified clusters.

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review

BACKGROUND

Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies.

METHODS

A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models.

RESULTS

From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy.

CONCLUSIONS

Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.

Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal

Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved, a phenomenon commonly known as right censoring, which renders the analysis of these data challenging. Over the years, machine learning algorithms have been developed and adapted to right-censored data. Neural networks have been repeatedly employed to build clinical prediction models in healthcare with a focus on cancer and cardiology. We present the first ever attempt at a large-scale review of survival neural networks (SNNs) with prognostic factors for clinical prediction in medicine. This work provides a comprehensive understanding of the literature (24 studies from 1990 to August 2021, global search in PubMed). Relevant manuscripts are classified as methodological/technical (novel methodology or new theoretical model; 13 studies) or applications (11 studies). We investigate how researchers have used neural networks to fit survival data for prediction. There are two methodological trends: either time is added as part of the input features and a single output node is specified, or multiple output nodes are defined for each time interval. A critical appraisal of model aspects that should be designed and reported more carefully is performed. We identify key characteristics of prediction models (i.e., number of patients/predictors, evaluation measures, calibration), and compare ANN's predictive performance to the Cox proportional hazards model. The median sample size is 920 patients, and the median number of predictors is 7. Major findings include poor reporting (e.g., regarding missing data, hyperparameters) as well as inaccurate model development/validation. Calibration is neglected in more than half of the studies. Cox models are not developed to their full potential and claims for the performance of SNNs are exaggerated. Light is shed on the current state of art of SNNs in medicine with prognostic factors. Recommendations are made for the reporting of clinical prediction models. Limitations are discussed, and future directions are proposed for researchers who seek to develop existing methodology.

An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts

BACKGROUND

Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC.

METHODS

Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation.

RESULTS

In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients.

CONCLUSIONS

The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Background

Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients.

Methods

A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model.

Results

The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance.

Conclusions

The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.

Liver Metastasis Formation Is Defined by AMIGO2 Expression via Adhesion to Hepatic Endothelial Cells in Human Gastric and Colorectal Cancer Cells

The adhesion of circulating cancer cells to vascular endothelial cells is an initial and critical step in distant metastases. Amphoterin-induced gene and open reading frame 2 (AMIGO2) was found to regulate tumor cell adhesion to hepatic endothelial cells and act as a driver gene for liver metastasis in mouse cell lines. However, whether the role of AMIGO2 observed in mouse tumor cells can be extrapolated to human cancer cells in vivo has not been verified. In this study, AMIGO2 expression in various human gastric and colorectal cancer cells was found to be closely associated with their adhesion to human hepatic sinusoidal endothelial cells (HHSECs). Constitutive AMIGO2-knockdown clones of human gastric (MKN-45) and colorectal cancer cell lines (DLD-1) were established to examine whether AMIGO2 expression in cancer cells is involved in the adhesion to HHSECs in vitro and the formation of liver metastasis in vivo. All AMIGO2-knockdown cells showed significantly attenuated adhesion to HHSECs. In vivo analysis revealed that intrasplenic inoculation of AMIGO2-knockdown clones could engraft in the spleen but significantly suppressed liver metastasis in nude mice. This study demonstrated that the role of AMIGO2 as a driver gene of liver metastasis in mouse tumor cells can be extrapolated to human cancer cells.

Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

BACKGROUND

In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality.

OBJECTIVE

This study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer.

METHODS

We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies.

RESULTS

We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size.

CONCLUSIONS

We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.

A nomogram model for predicting prognosis of obstructive colorectal cancer

Background

The prognosis of obstructive colorectal cancer (oCRC) is worse than that of nonobstructive colorectal cancer. However, no previous study has established an individualized prediction model for the prognosis of patients with oCRC. We aimed to screen the factors that affect the prognosis of oCRC and to use these findings to establish a nomogram model that predicts the individual prognosis of patients with oCRC.

Methods

This retrospective study collected data of 181 patients with oCRC from three medical hospitals between February 2012 and December 2017. Among them, 129 patients from one hospital were used as the training cohort. Univariate and multivariate analyses were used in this training cohort to select independent risk factors that affect the prognosis of oCRC, and a nomogram model was established. The other 52 patients from two additional hospitals were used as the validation cohort to verify the model.

Results

Multivariate analysis showed that carcinoembryonic antigen level (p = 0.037, hazard ratio [HR] = 2.872 [1.065–7.740]), N stage (N1 vs. N0, p = 0.028, HR = 3.187 [1.137–8.938]; N2 vs. N0, p = 0.010, HR = 4.098 [1.393–12.051]), and surgical procedures (p = 0.002, HR = 0.299 [0.139–0.643]) were independent prognostic factors of overall survival in patients with oCRC. These factors were used to construct the nomogram model, which showed good concordance and accuracy.

Conclusion

Carcinoembryonic antigen, N stage, and surgical method are independent prognostic factors for overall survival in patients with oCRC, and the nomogram model can visually display these results.

Application of an artificial neural network for predicting the potential chemotherapy benefit of patients with gastric cancer after radical surgery

BACKGROUND

Artificial neural network models have a strong self-learning ability and can deal with complex biological information, but there is no artificial neural network model for predicting the benefits of adjuvant chemotherapy in patients with gastric cancer.

METHODS

The clinicopathological data of patients who underwent radical resection of gastric cancer from January 2010 to September 2014 were analyzed retrospectively. Patients who underwent surgery combined with adjuvant chemotherapy were randomly divided into a training cohort (70%) and a validation cohort (30%). An artificial neural network model (potential-CT-benefit-ANN) was established, and its ability to predict the potential benefit of chemotherapy was evaluated by the C-index. The prognostic prediction and stratification ability of potential-CT-benefit-ANN and the eighth American Joint Committee on Cancer staging system were compared by receiver operating characteristic curves and Kaplan-Meier curves.

RESULTS

In both the training and validation cohort, potential-CT-benefit-ANN shows good prediction accuracy for potential adjuvant chemotherapy benefit. The receiver operating characteristic curve showed that the prediction accuracy of potential-CT-benefit-ANN was better than that of the eighth American Joint Committee on Cancer staging system in all groups. The calibration plots showed that the predicted prognosis of potential-CT-benefit-ANN was highly consistent with the actual value. The survival curves showed that potential-CT-benefit-ANN could stratify prognosis well for all groups and performed significantly better than the eighth AJCC staging system.

CONCLUSION

The potential-CT-benefit-ANN model developed in this study can accurately predict the potential benefits of adjuvant chemotherapy in patients with stage II/III gastric cancer. The benefit score based on potential-CT-benefit-ANN can predict the long-term prognosis of patients with adjuvant chemotherapy and has good prognostic stratification ability.

Artificial intelligence in gastric cancer: a translational narrative review

Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.

External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival

Purpose

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality.

Materials and Methods

The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS).

Results

Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation.

Conclusion

The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Application of artificial intelligence in the diagnosis and prediction of gastric cancer

Gastric cancer is the second leading cause of cancer deaths worldwide. Despite the great progress in the diagnosis and treatment of gastric cancer, the incidence and mortality rate of the disease in China are still relatively high. The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis. Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer, in order to more accurately predict the survival times of gastric cancer patients. Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer. In the past decade, artificial intelligence technology has been applied to various fields of medicine as a branch of computer science. This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

BACKGROUND

Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN-based survival prediction model for patients with gastric cancer.

METHODS

Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time-dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.

RESULTS

An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5-year survival prediction, respectively. In the calibration curve analysis, the ANN-predicted survival had a high consistency with the actual survival. Comparison of the DCA and time-dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.

CONCLUSIONS

The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Artificial intelligence in gastric cancer: a systematic review

OBJECTIVE

This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer.

METHODS

A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications.

RESULTS

A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance.

CONCLUSIONS

In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.

Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects

Gastric cancer is the fifth malignancy and the third cause of cancer death worldwide, according to the global cancer statistics presented in 2018. Its definition and staging have been revised in the eight edition of the AJCC/TNM classification, which took effect in 2018. Novel molecular classifications for GC have been recently established and the process of translating these classifications into clinical practice is ongoing. The cornerstone of GC treatment is surgical, in a context of multimodal therapy. Surgical treatment is being standardized, and is evolving according to new anatomical concepts and to the recent technological developments. This is leading to a massive improvement in the use of mini-invasive techniques. Mini-invasive techniques aim to be equivalent to open surgery from an oncologic point of view, with better short-term outcomes. The persecution of better short-term outcomes also includes the optimization of the perioperative management, which is being implemented on large scale according to the enhanced recovery after surgery principles. In the era of precision medicine, multimodal treatment is also evolving. The long-time-awaited results of many trials investigating the role for preoperative and postoperative management have been published, changing the clinical practice. Novel investigations focused both on traditional chemotherapeutic regimens and targeted therapies are currently ongoing. Modern platforms increase the possibility for further standardization of the different treatments, promote the use of big data and open new possibilities for surgical learning. This systematic review in two parts assesses all the current updates in GC treatment.

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

BACKGROUND

We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis.

METHODS

The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AUCs), as was the concordance index (c-index).

RESULTS

The subjects (n = 1088) were separated into training (80%, n = 870) and test sets (20%, n = 218). The training data were randomly sorted into training and validation sets using 5-fold cross validation. The median c-index of the five validation sets was 0.84 (95% confidence interval 0.79-0.87). The median AUC of the five validation subsets was 0.84. This model was evaluated with the previously separated test set. The c-index was 0.82 and the mean AUC of the 30 different time windows was 0.85 (standard deviation 0.02). According to the estimated survival probability (by 62 months), we divided the test group into five subgroups. The survival curves of the subgroups showed statistically significant separation (p < 0.001).

CONCLUSIONS

This study is the first to analyze population-level data using artificial neural network ML algorithms for the role and outcomes of surgical resection and radiation therapy in spino-pelvic chondrosarcoma.

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

The Role of Artificial Intelligence in the Prediction of Functional Maturation of Arteriovenous Fistula

Objective: The aim of this study is to examine the application of virtual artificial intelligence (AI) in the prediction of functional maturation (FM) and pattern recognition of factors in autogenous radiocephalic arteriovenous fistula (RCAVF) formation. Materials and Methods: A prospective database of 266 individuals over a four-year period with n=10 variables were used to train, validate and test an artificial neural network (ANN). The ANN was constructed to create a predictive model and evaluate the impact of variables on the endpoint of FM. Results: The overall accuracy of the training, validation, testing and all data on each output matrix at detecting FM was 86.4%, 82.5%, 77.5% and 84.5%, respectively. The results corresponded with their area under the curve for each output matrix at best sensitivity and at 1-specificity with the log-rank test p<0.01. ANN classification identified age, artery and vein diameter to influence FM with an accuracy of (>89%). AI has the ability of predicting with a high grade of accuracy FM and recognising patterns that influence it. Conclusion: AI is a replicable tool that could remain up to date and flexible to ongoing deep learning with further data feed ensuring substantial enhancement in its accuracy. AI could serve as a clinical decision-making tool and its application in vascular access requires further evaluation.