PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer

Transformative artificial intelligence in gastric cancer: Advancements in diagnostic techniques

Gastric cancer represents a significant global health challenge with elevated incidence and mortality rates, highlighting the need for advancements in diagnostic and therapeutic strategies. This review paper addresses the critical need for a thorough synthesis of the role of artificial intelligence (AI) in the management of gastric cancer. It provides an in-depth analysis of current AI applications, focusing on their contributions to early diagnosis, treatment planning, and outcome prediction. The review identifies key gaps and limitations in the existing literature by examining recent studies and technological developments. It aims to clarify the evolution of AI-driven methods and their impact on enhancing diagnostic accuracy, personalizing treatment strategies, and improving patient outcomes. The paper emphasizes the transformative potential of AI in overcoming the challenges associated with gastric cancer management and proposes future research directions to further harness AI's capabilities. Through this synthesis, the review underscores the importance of integrating AI technologies into clinical practice to revolutionize gastric cancer management.

Nuclear medicine based multimodal molecular imaging facilitates precision medicine for gastrointestinal tumors

Gastric and colorectal cancers are the most common gastrointestinal malignancies, with high morbidity and mortality rates. Early diagnosis and accurate staging are of great significance for formulating reasonable clinical treatment plans, guiding surgical methods, effectively carrying out individualized comprehensive treatment, and estimating prognosis. As representatives of nuclear medicine based multimodal molecular imaging technologies, positron emission tomography/computed tomography and positron emission tomography/magnetic resonance imaging allow for obtaining the status of lesions throughout the body in one imaging procedure, and are less likely to miss distant and neighboring metastatic lesions. It is very important to truly achieve accurate disease classification and diagnosis, and develop individualized disease prevention and treatment plans. The emerging multimodal nuclide tracer molecular imaging technology has important clinical value in the diagnosis and treatment of gastric cancer and colorectal cancer. This article reviews the application and progress of the two examination methods in the diagnosis and staging of gastric cancer and colorectal cancer.

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms

Background

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model.

Objective

Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer.

Methods

A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output.

Results

Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy.

Conclusion

In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma

Objectives

This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies.

Methods

A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

Results

Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit.

Conclusions

Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

The lncRNA CADM2-AS1 promotes gastric cancer metastasis by binding with miR-5047 and activating NOTCH4 translation

Background

Multi-organ metastasis has been the main cause of death in patients with Gastric cancer (GC). The prognosis for patients with metastasized GC is still very poor. Long noncoding RNAs (lncRNAs) always been reported to be closely related to cancer metastasis.

Methods

In this paper, the aberrantly expressed lncRNA CADM2-AS1 was identified by lncRNA-sequencing in clinical lymph node metastatic GC tissues. Besides, the role of lncRNA CADM2-AS1 in cancer metastasis was detected by Transwell, Wound healing, Western Blot or other assays in vitro and in vivo. Further mechanism study was performed by RNA FISH, Dual-luciferase reporter assay and RT-qPCR. Finally, the relationship among lncRNA CADM2-AS1, miR-5047 and NOTCH4 in patient tissues was detected by RT-qPCR.

Results

In this paper, the aberrantly expressed lncRNA CADM2-AS1 was identified by lncRNA-sequencing in clinical lymph node metastatic GC tissues. Besides, the role of lncRNA CADM2-AS1 in cancer metastasis was detected in vitro and in vivo. The results shown that overexpression of the lncRNA CADM2-AS1 promoted GC metastasis, while knockdown inhibited it. Further mechanism study proved that lncRNA CADM2-AS1 could sponge and silence miR-5047, which targeting mRNA was NOTCH4. Elevated expression of lncRNA CADM2-AS1 facilitate GC metastasis by up-regulating NOTCH4 mRNA level consequently. What's more, the relationship among lncRNA CADM2-AS1, miR-5047 and NOTCH4 was further detected and verified in metastatic GC patient tissues.

Conclusions

LncRNA CADM2-AS1 promoted metastasis in GC by targeting the miR-5047/NOTCH4 signaling axis, which may be a potential target for GC metastasis.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis

BACKGROUND

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC).

PURPOSE

Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC.

METHODS

The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model.

RESULTS

The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility.

CONCLUSIONS

18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Ultrasensitive detection of uveal melanoma using [18F]AlF-NOTA-PRGD2 PET imaging

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, and early detection is critical to improve the clinical outcome of this disease. In this study, the diagnostic effectiveness of [18F]AlF-NOTA-PRGD2 (an investigational medicinal product) positron emission tomography (PET) imaging in UM xenografts and UM patients were evaluated. The cell uptake, cell binding ability and in vitro stability of [18F]AlF-NOTA-PRGD2 were evaluated in 92-1 UM cell line. MicroPET imaging and biodistribution study of [18F]AlF-NOTA-PRGD2 were conducted in 92-1 UM xenografts. Then, UM patients were further recruited for evaluating the diagnostic effectiveness of [18F]AlF-NOTA-PRGD2 PET imaging (approval no. NCT02441972 in clinicaltrials.gov). In addition, comparison of [18F]AlF-NOTA-PRGD2 and 18F-labelled fluorodeoxyglucose ([18F]FDG) PET imaging in UM xenografts and UM patients were conducted.

RESULTS

The in vitro data showed that [18F]AlF-NOTA-PRGD2 had a high cell uptake, cell binding ability and in vitro stability in 92-1 UM cell line. The in vivo data indicated that 92-1 UM tumors were clearly visualized with the [18F]AlF-NOTA-PRGD2 tracer in the subcutaneous and ocular primary UM xenografts model at 60 min post-injection. And the tumor uptake of the tracer was 2.55 ± 0.44%ID/g and 1.73 ± 0.15%ID/g at these two tissue locations respectively, at 7 days after animal model construction. The clinical data showed that tumors in UM patients were clearly visualized with the [18F]AlF-NOTA-PRGD2 tracer at 60 min post-injection. In addition, [18F]AlF-NOTA-PRGD2 tracer showed higher sensitivity and specificity for PET imaging in UM xenografts and UM patients compared to [18F]FDG tracer.

CONCLUSION

[18F]AlF-NOTA-PRGD2 PET imaging may be a more preferred approach in the diagnosis of primary UM compared to [18F]FDG PET imaging. Additionally, due to the high tumor-to-background ratio, [18F]AlF-NOTA-PRGD2 PET imaging seems also to be applicable for the diagnosis of UM patients with liver metastasis.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02441972, Registered 1 January 2012, https://clinicaltrials.gov/study/NCT02441972 .

Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study

BACKGROUND

Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis.

AIM

To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis.

METHODS

The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study. There were 561 males and 450 females. The mean age was 58 ± 11 years. The patient underwent radical gastrectomy. The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens. The outcomes were as follows: (1) Lymph node metastasis in EGC patients; (2) Analysis of influencing factors of lymph node metastasis in EGC; and (3) Analysis of prognostic factors in patients with EGC. Normally distributed measurement data are expressed as mean ± SD, and a t test was used for comparisons between groups. The data are expressed as absolute numbers or percentages, and the chi-square test was used for comparisons between groups. Rank data were compared using a nonparametric rank sum test. A log-rank test and a logistic regression model were used for univariate analysis. A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis. The Kaplan-Meier method was used to calculate the survival rate and construct survival curves. A log-rank test was used for survival analysis.

RESULTS

Analysis of influencing factors of lymph node metastasis in EGC. The results of the multifactor analysis showed that tumor length and diameter, tumor site, tumor invasion depth, vascular thrombus, and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC (odds ratios = 1.80, 1.49, 2.65, 5.76, and 0.60; 95%CI: 1.29-2.50, 1.11-2.00, 1.81-3.88, 3.87-8.59, and 0.48-0.76, respectively; P < 0.05). Analysis of prognostic factors in patients with EGC. All 1011 patients with EGC were followed up for 43 (0-13) months. The 3-year overall survival rate was 97.32%. Multivariate analysis revealed that age > 60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC (hazard ratio = 9.50, 2.20; 95%CI: 3.31-27.29, 1.00-4.87; P < 0.05). Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged > 60 years and ≤ 60 years were 99.37% and 94.66%, respectively, and the difference was statistically significant (P < 0.05). The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42% and 97.92%, respectively, and the difference was statistically significant (P < 0.05).

CONCLUSION

The lymph node metastasis rate of EGC patients was 23.64%. Tumor length, tumor site, tumor infiltration depth, vascular cancer thrombin, and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients. Age > 60 years and lymph node metastasis are independent risk factors for EGC prognosis.

A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients

BACKGROUND

At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive.

OBJECTIVE

This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients.

METHODS

A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method.

RESULTS

Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models.

CONCLUSIONS

This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer

OBJECTIVE

This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III).

METHODS

This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA).

RESULTS

A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility.

CONCLUSION

The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Pharmacokinetic Study of Ultrasmall Superparamagnetic Iron Oxide Nanoparticles HY-088 in Rats

BACKGROUND AND OBJECTIVE

HY-088 injection is an ultrasmall superparamagnetic iron oxide nanoparticle (USPIOs) composed of iron oxide crystals coated with polyacrylic acid (PAA) on the surface. The purpose of this study was to investigate the pharmacokinetics, tissue distribution, and mass balance of HY-088 injection.

METHODS

The pharmacokinetics of [55Fe]-HY-088 and [14C]-HY-088 were investigated in 48 SD rats by intravenous injection of 8.5 (low-dose group), 25.5 (medium-dose group), and 85 (high-dose group) mg/100 μCi/kg. Tissue distribution was studied by intravenous injection of 35 mg/100 μCi/kg in 48 SD rats, and its tissue distribution in vivo was obtained by ex vivo tissue assay. At the same time, [14C]-HY-088 was injected intravenously at a dose of 25.5 mg/100 μCi/kg into 16 SD rats, and its tissue distribution in vivo was studied by quantitative whole-body autoradiography. [14C]-HY-088 and [55Fe]-HY-088 were injected intravenously into 24 SD rats at a dose of 35 mg/100 μCi/kg, and their metabolism was observed.

RESULTS

In the pharmacokinetic study, [55Fe]-HY-088 reached the maximum observed concentration (Cmax) at 0.08 h in the low- and medium-dose groups of SD rats. [14C]-HY-088 reached Cmax at 0.08 h in the three groups of SD rats. The area under the concentration-time curve (AUC) of [55Fe]-HY-088 and [14C]-HY-088 increased with increasing dose. In the tissue distribution study, [55Fe]-HY-088 and [14C]-HY-088 were primarily distributed in the liver, spleen, and lymph nodes of both female and male rats. In the mass balance study conducted over 57 days, the radioactive content of 55Fe from [55Fe]-HY-088 was primarily found in the carcass, accounting for 86.42 ± 4.18% in females and 95.46 ± 6.42% in males. The radioactive recovery rates of [14C]-HY-088 in the urine of female and male rats were 52.99 ± 5.48% and 60.66 ± 2.23%, respectively.

CONCLUSIONS

Following single intravenous administration of [55Fe]-HY-088 and [14C]-HY-088 in SD rats, rapid absorption was observed. Both [55Fe]-HY-088 and [14C]-HY-088 were primarily distributed in the liver, spleen, and lymph nodes. During metabolism, the radioactivity of [55Fe]-HY-088 is mainly present in the carcass, whereas the 14C-labeled [14C]-HY-088 shell PAA is eliminated from the body mainly through the urine.

The Utility of 18F-FDG-PET-CT Metabolic Parameters in Evaluating the Primary Tumor Aggressiveness and Lymph Node Metastasis of Nasopharyngeal Carcinoma

Background

Following changes in primary tumor (T) and lymph node (N) staging for nasopharyngeal carcinoma (NPC) in the Eighth Edition AJCC Cancer Staging Manual, simplification of T staging has been proposed. However, a limited range of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography-computed tomography (18F-FDG PET-CT) metabolic parameters has been investigated. Therefore, we aimed to evaluate the primary tumor invasiveness and the lymph node metastasis (LNM) of NPC from a metabolic perspective.

Methods

A total of 435 NPC patients underwent 18F-FDG PET/CT before treatment were retrospectively examined. The primary endpoint was differences in standard uptake value (SUV), lean body mass-normalized SUV (SUL), body surface area-normalized SUV (SUS), glucose-normalized SUV (GN), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and glucose-normalized total lesion glycolysis (GNTLG) of primary tumors and LNM between different T and N stages. The metabolic parameters associated with T and N staging were identified.

Results

There were significant differences between all parameters relative to the primary tumor but no significant differences in any parameter relative to the LNM and T stages. Higher mean values of TGNmax, TGNmean, TSUVpeak, and TSUSmax were associated with advanced T stages. Higher mean values of all the LNM parameters were associated with more advanced N stages. Only primary tumor metabolic tumor volume (TMTV), TSUVpeak, TSULmax, and TSUSmax showed a significant positive association with T staging, while lymph node metabolic tumor volume (LNMTV) and TSUSmax were significantly positive in N staging.

Conclusions

Our findings suggest that metabolic parameters are useful indicators of tumor invasiveness and LNM based on the Eighth Edition manual. Compared with volume-dependent parameters, TGNmax, TGNmean, TSUVpeak, and TSUSmax may be better indicators of local tumor aggressiveness. SUSmax of the primary tumor was associated with LNM. In addition to SUVmax, other metabolic parameters (eg, SULmax, SUSmax, GNmax, and GNmean) could evaluate tumor aggressiveness and LNM better.

Prognostic factors of resectable anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) patients: a retrospective analysis based on a single center

Background

Anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) exhibited a higher propensity for lymph node metastasis (LNM). This study aimed to investigate risk factors of occult lymph node metastasis (OLNM) and recurrence in resectable ALK-rearranged NSCLC patients.

Methods

This retrospective analysis included patients with ALK-rearranged NSCLC receiving lung resections at Shanghai Pulmonary Hospital from June 2016 to August 2021. Logistic regression analysis was used to ascertain predictors of OLNM, and Cox regression analysis to identify risk factors of recurrence.

Results

A total of 603 resectable ALK-rearranged NSCLC patients were included. The mean age was 55 years old. There were 171 patients (28.4%) pathologically confirmed to have LNM, 51.5% of which were occult. Logistic regression analysis identified clinical tumor size and computed tomography (CT) density as independent factors for OLNM. Cox regression analysis showed that pleural invasion and pathological tumor size were independent prognosticators for recurrence in pathologically nodal negative patients. Among pathologically nodal positive patients, adjuvant ALK-tyrosine kinase inhibitors (TKI) showed a similar recurrence-free survival (RFS) to chemotherapy (hazard ratio, 0.454; 95% confidence interval, 0.111-1.864).

Conclusions

Assessing the potential risk of OLNM is required for ALK-rearranged NSCLC patients with large tumors characterized by high CT densities. Patients with large pathological tumor size or pleural infiltration should be closely monitored despite being pathologically nodal negative. Additionally, adjuvant ALK-TKI may present a comparable RFS to chemotherapy in pathologically nodal positive patients.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND

Gastric cancer is one of the most common malignant tumors in the digestive system, ranking sixth in incidence and fourth in mortality worldwide. Since 42.5% of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type, the application of imaging diagnosis is restricted.

AIM

To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) algorithms and to evaluate their predictive performance in clinical practice.

METHODS

Data of a total of 369 patients who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Seven ML models, including decision tree, random forest, support vector machine (SVM), gradient boosting machine, naive Bayes, neural network, and logistic regression, were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML models were established following ten cross-validation iterations using the training dataset, and subsequently, each model was assessed using the test dataset. The models' performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.

RESULTS

Among the seven ML models, except for SVM, the other ones exhibited higher accuracy and reliability, and the influences of various risk factors on the models are intuitive.

CONCLUSION

The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer, which can aid in personalized clinical diagnosis and treatment.

Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study

OBJECTIVES

This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice.

METHODS

Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.

RESULTS

Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive.

CONCLUSION

For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach

Through meticulous examination of lymph nodes, the stage and severity of cancer can be determined. This information is invaluable for doctors to select the most appropriate treatment plan and predict patient prognosis; however, any oversight in the examination of lymph nodes may lead to cancer metastasis and poor prognosis. In this review, we summarize a significant number of articles supported by statistical data and clinical experience, proposing a standardized evaluation protocol for lymph nodes. This protocol begins with preoperative imaging to assess the presence of lymph node metastasis. Radiomics has replaced the single-modality approach, and deep learning models have been constructed to assist in image analysis with superior performance to that of the human eye. The focus of this review lies in intraoperative lymphadenectomy. Multiple international authorities have recommended specific numbers for lymphadenectomy in various cancers, providing surgeons with clear guidelines. These numbers are calculated by applying various statistical methods and real-world data. In the third chapter, we mention the growing concern about immune impairment caused by lymph node dissection, as the lack of CD8 memory T cells may have a negative impact on postoperative immunotherapy. Both excessive and less lymph node dissection have led to conflicting findings on postoperative immunotherapy. In conclusion, we propose a protocol that can be referenced by surgeons. With the systematic management of lymph nodes, we can control tumor progression with the greatest possible likelihood, optimize the preoperative examination process, reduce intraoperative risks, and improve postoperative quality of life.

Application of CD34 expression combined with three-phase dynamic contrast-enhanced computed tomography scanning in preoperative staging of gastric cancer

BACKGROUND

Accurate preoperative staging of gastric cancer (GC), a common malignant tumor worldwide, is critical for appropriate treatment plans and prognosis. Dynamic three-phase enhanced computed tomography (CT) scanning for preoperative staging of GC has limitations in evaluating tumor angiogenesis. CD34, a marker on vascular endothelial cell surfaces, is promising in evaluating tumor angiogenesis. We explored the value of their combination for preoperative staging of GC to improve the efficacy and prognosis of patients with GC.

AIM

To explore the evaluation value of CD34 expression + dynamic three-phase enhanced CT scanning in preoperative staging of GC.

METHODS

Medical records of 106 patients with GC treated at the First People's Hospital of Lianyungang between February 2021 and January 2023 were retrospectively studied. All patients underwent three-phase dynamic contrast-enhanced CT scanning before surgery, and CD34 was detected in gastroscopic biopsy specimens. Using surgical and pathological results as the gold standard, the diagnostic results of three-phase dynamic contrast-enhanced CT scanning at different T and N stages were analyzed, and the expression of CD34-marked microvessel density (MVD) at different T and N stages was determined. The specificity and sensitivity of three-phase dynamic contrast-enhanced CT and CD34 in T and N staging were calculated; those of the combined diagnosis of the two were evaluated in parallel. Independent factors affecting lymph node metastasis were analyzed using multiple logistic regression.

RESULTS

The accuracy of three-phase dynamic contrast-enhanced CT scanning in diagnosing stages T1, T2, T3 and T4 were 68.00%, 75.00%, 79.41%, and 73.68%, respectively, and for diagnosing stages N0, N1, N2, and N3 were 75.68%, 74.07%, 85.00%, and 77.27%, respectively. CD34-marked MVD expression increased with increasing T and N stages. Specificity and sensitivity of three-phase dynamic contrast-enhanced CT in T staging were 86.79% and 88.68%; for N staging, 89.06% and 92.86%; for CD34 in T staging, 64.15% and 88.68%; and for CD34 in N staging, 84.38% and 78.57%, respectively. Specificity and sensitivity of joint diagnosis in T staging were 55.68% and 98.72%, and N staging were 75.15% and 98.47%, respectively, with the area under the curve for diagnosis improving accordingly. According to multivariate analysis, a longer tumor diameter, higher pathological T stage, lower differentiation degree, and higher expression of CD34-marked MVD were independent risk factors for lymph node metastasis in patients with GC.

CONCLUSION

With high accuracy in preoperatively determining the invasion depth and lymph node metastasis of GC, CD34 expression and three-phase dynamic contrast-enhanced CT can provide a reliable basis for surgical resection.

Imaging in Gastric Cancer: Current Practice and Future Perspectives

Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.

CT-based radiomics nomogram for differentiating gastric hepatoid adenocarcinoma from gastric adenocarcinoma: a multicentre study

BACKGROUND

To develop a CT-based radiomics nomogram for the high-precision preoperative differentiation of gastric hepatoid adenocarcinoma (GHAC) patients from gastric adenocarcinoma (GAC) patients.

RESEARCH DESIGN AND METHODS

108 patients with GHAC from 6 centers and 108 GAC patients matched by age, sex and T stage undergoing pathological examination were retrospectively reviewed. Patients from 5 centers were divided into two cohorts (training and internal validation) at a 7:3 ratio, the remaining patients were external test cohort. Venous-phase CT images were retrieved for tumor segmentation and feature extraction. A radiomics model was developed by the least absolute shrinkage and selection operator method. The nomogram was developed by clinical factors and the radiomics score.

RESULTS

1409 features were extracted and a radiomics model consisting of 19 features was developed, which showed a favorable performance in discriminating GHAC from GAC (AUC_{training cohort} = 0.998, AUC_{internal validation set} = 0.942, AUC_{external test cohort} = 0.731). The radiomics nomogram, including the radiomics score, AFP, and CA72_4, achieved good calibration and discrimination (AUC_{training cohort} = 0.998, AUC_{internal validation set} = 0.954, AUC_{external test cohort} = 0.909).

CONCLUSIONS

The noninvasive CT-based nomogram, including radiomics score, AFP, and CA72_4, showed favorable predictive efficacy for differentiating GHAC from GAC and might be useful for clinical decision-making.