CT Perfusion evaluation of gastric cancer: correlation with histologic type

Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace

Objective

This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis.

Methods

The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field.

Results

A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC.

Conclusions

Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

BACKGROUND

The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades.

METHODS

A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared.

RESULTS

Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis.

CONCLUSION

DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide. Precise diagnosis and evaluation of GC, especially using noninvasive methods, are fundamental to optimal therapeutic decision-making. Despite the recent rapid advancements in technology, pretreatment diagnostic accuracy varies between modalities, and correlations between imaging and histological features are far from perfect. Artificial intelligence (AI) techniques, particularly hand-crafted radiomics and deep learning, have offered hope in addressing these issues. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes (data acquisition, lesion segmentation, feature extraction, feature selection, and model construction) involved in AI. We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction. Challenges and opportunities in AI-based GC research are highlighted for consideration in future studies.

Survival and adequate preoperative staging in patients undergoing gastric cancer surgery at a Peruvian Police Hospital

INTRODUCTION

Gastric cancer is the fifth most common malignant neoplasm and the third leading cause of cancer-related death worldwide. In Peru, its incidence is 15.8 per 100,000 population, and it is associated with high mortality rates, especially in areas with low socioeconomic status. The aim of this study was to compare preoperative, postoperative, and anatomopathological staging results and their relation to disease recurrence and survival.

METHODS

We conducted a retrospective cohort study of patients undergoing surgery for gastric cancer with a definitive postoperative anatomopathological diagnosis from 2005 to 2014 at the Hospital Nacional Luis N. Sáenz. Statistical analyses included descriptive and correlation statistics using the κ index, determination of associations between preoperative and postoperative staging and surgical reintervention and recurrence using the χ²  test, as well as Kaplan Meier survival analysis.

RESULTS

There was little correlation between preoperative staging and final anatomopathological diagnosis, while there was a good correlation with postoperative staging. A significant association was found between preoperative staging and cancer recurrence. In the survival analysis, survival was lower among patients with underestimated staging.

CONCLUSIONS

The survival of patients with gastric cancer can be affected by an overestimation of preoperative staging, therefore improvements in preoperative staging could lengthen the survival of patients undergoing gastric cancer surgery.

Diagnosis of gastric cancer by MDCT gastrography: diagnostic characteristics and management potential

Background

Gastric cancer is regarded as the fifth most frequent tumor globally but the third most common fatal illness. As early as possible, we diagnose cancer stomach especially at early stages, the higher the rate of life. Nevertheless, most cases are diagnosed at late cases where surgery is not of the same benefit at early stages because of clinically indefinite symptoms. The prospective study goal is to estimate the role of MDCT in diagnosis and staging of cancer stomach.

Results

In our study, it was found that there was a high relationship between pathological and CT staging by using MPR. CT with MPR was specific and accurate in diagnosis of all stages of gastric cancer with specificity ranged between 93 and 97% and accuracy ranged between 90 and 92.5%. However, it showed lowest sensitivity in diagnosis of stage 1 of gastric cancer. On the other hand, it showed highest sensitivity (90%) in diagnosis of stage IV as well as we found that MPR and VR of MDCT are much more accurate (92.5%) than multi-detector computed tomography axial images (80%) in the diagnosis of all stages of gastric cancer with the difference between the two sequences was significant (P = 0.009).

Conclusion

Our results demonstrate that preoperative MDCT with contrast filling technique for abdomen and pelvis evaluates the local disease process of gastric cancer as well as the potential areas of spread. This information is vital in choosing between palliative or radical surgery. MPR and VR help in the assessment of tumor extension and considered as a highly representative prognostic value. Making it the imaging modality of choice in diagnosis and staging of gastric cancers.

Dynamic Contrast-Enhanced Ultrasound of Gastric Cancer: Correlation with Perfusion CT and Histopathology

Objective

To assess the relationship between contrast-enhanced ultrasound (CEUS) parameters and perfusion CT (PCT) parameters of gastric cancers and their correlation with histologic features.

Materials and Methods

This prospective study was approved by our Institutional Review Board. We included 43 patients with pathologically-proven gastric cancers undergoing CEUS using SonoVue® (Bracco) and PCT on the same day. Correlation between the CEUS parameters (peak intensity [PI], area under the curve [AUC], rise time [RT] from 10% to 90% of PI, time to peak [TTP_US], and mean transit time [MTT_US]) and PCT parameters (blood flow, blood volume, TTP_CT, MTT_CT, and permeability surface product) of gastric cancers were analyzed using Spearman's rank correlation test. In cases of surgical resection, the CEUS and PCT parameters were compared according to histologic features using Mann-Whitney test.

Results

CEUS studies were of diagnostic quality in 88.4% (38/43) of patients. Among the CEUS parameters of gastric cancers, RT and TTP_US showed significant positive correlations with TTP_CT (rho = 0.327 and 0.374, p = 0.045 and 0.021, respectively); PI and AUC were significantly higher in well-differentiated or moderately-differentiated tumors (n = 4) than poorly-differentiated tumors (n = 18) (p = 0.026 and 0.033, respectively), whereas MTT_CT showed significant differences according to histologic types (poorly cohesive carcinoma [PCC] vs. non-PCC), T-staging (≤ T2 vs. ≥ T3), N-staging (N0 vs. N-positive), and epidermal growth factor receptor expression (≤ faint vs. ≥ moderate staining) (p values < 0.05).

Conclusion

In patients with gastric cancers, CEUS is technically feasible for the quantification of tumor perfusion and may provide correlative and complementary information to that of PCT, which may allow prediction of histologic features.

Prediction of Treatment Outcome of Chemotherapy Using Perfusion Computed Tomography in Patients with Unresectable Advanced Gastric Cancer

Objective

To evaluate whether data acquired from perfusion computed tomography (PCT) parameters can aid in the prediction of treatment outcome after palliative chemotherapy in patients with unresectable advanced gastric cancer (AGC).

Materials and Methods

Twenty-one patients with unresectable AGCs, who underwent both PCT and palliative chemotherapy, were prospectively included. Treatment response was assessed according to Response Evaluation Criteria in Solid Tumors version 1.1 (i.e., patients who achieved complete or partial response were classified as responders). The relationship between tumor response and PCT parameters was evaluated using the Mann-Whitney test and receiver operating characteristic analysis. One-year survival was estimated using the Kaplan-Meier method.

Results

After chemotherapy, six patients exhibited partial response and were allocated to the responder group while the remaining 15 patients were allocated to the non-responder group. Permeability surface (PS) value was shown to be significantly different between the responder and non-responder groups (51.0 mL/100 g/min vs. 23.4 mL/100 g/min, respectively; p = 0.002), whereas other PCT parameters did not demonstrate a significant difference. The area under the curve for prediction in responders was 0.911 (p = 0.004) for PS value, with a sensitivity of 100% (6/6) and specificity of 80% (12/15) at a cut-off value of 29.7 mL/100 g/min. One-year survival in nine patients with PS value > 29.7 mL/100 g/min was 66.7%, which was significantly higher than that in the 12 patients (33.3%) with PS value ≤ 29.7 mL/100 g/min (p = 0.019).

Conclusion

Perfusion parameter data acquired from PCT demonstrated predictive value for treatment outcome after palliative chemotherapy, reflected by the significantly higher PS value in the responder group compared with the non-responder group.

Detection of gastric cancer and its histological type based on iodine concentration in spectral CT

Background

Computed tomography (CT) imaging is the most common imaging modality for the diagnosis and staging of gastric cancer. The aim of this study is was to prospectively explore the ability of quantitative spectral CT parameters in the detection of gastric cancer and its histologic types.

Methods

A total of 87 gastric adenocarcinoma (43 poorly and 44 well-differentiated) patients and 36 patients with benign gastric wall lesions (25 inflammation and 11 normal), who underwent dual-phase enhanced spectral CT examination, were retrospectively enrolled in this study. Iodine concentration (IC) and normalized iodine concentration (nIC) during arterial phase (AP) and portal venous phase (PP) were measured thrice in each patient by two blinded radiologists. Moreover, intraclass correlation coefficient (ICC) was used to assess the interobserver reproducibility. Differences of IC and nIC values between gastric cancer and benign lesion groups were compared using Mann-Whitney U test. Furthermore, the gender, age, location, thickness and histological types of gastric adenocarcinoma were analyzed by Mann-Whitney U test or Kruskal-Wallis H test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of IC and nIC values, and the optimal cut-off value was calculated with Youden J.

Results

An excellent interobserver agreement (ICC >  0.6) was achieved for IC. Notably, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in gastric cancer group (Z = 5.870, 3.894, 2.009 and 10.137, respectively; P < 0.05) than those in benign lesion group. Additionally, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in poorly differentiated gastric adenocarcinoma group (Z = 4.118, 5.637, 6.729 and 2.950, respectively; P < 0.005) than those in well-differentiated gastric adenocarcinoma group. There were no statistically significant differences in the values of ICAP, ICPP, nICAP and nICPP between age, gender, tumor thickness and tumor location. Furthermore, the area under the curve (AUC) values of ICAP, nICAP, ICPP and nICPP were 0.745, 0.584, 0.662, and 0.932, respectively, for gastric cancer detection; while 0.756, 0.919, 0.851 and 0.684, respectively, in discriminating poorly differentiated gastric adenocarcinoma.

Conclusion

IC values exhibited great potential in the preoperative and non-invasive diagnosis of gastric cancer and its histological types. In particular, nICPP is more effective for the identification of gastric cancer, whereas nICAP is more effective in discriminating poorly differentiated gastric adenocarcinoma.

CT Perfusion in Patients with Lung Cancer: Squamous Cell Carcinoma and Adenocarcinoma Show a Different Blood Flow

Objectives

To characterize tumour baseline blood flow (BF) in two lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), also investigating those “borderline” cases whose perfusion value is closer to the group mean of the other histotype.

Materials and Methods

26 patients (age range 36-81 years) with primary Non-Small Cell Lung Cancer (NSCLC), subdivided into 19 AC and 7 SCC, were enrolled in this study and underwent a CT perfusion, at diagnosis. BF values were computed according to the maximum-slope method and unreliable values (e.g., arising from artefacts or vessels) were automatically removed. The one-tail Welch's t-test (p-value <0.05) was employed for statistical assessment.

Results

At diagnosis, mean BF values (in [mL/min/100g]) of AC group [(83.5 ± 29.4)] are significantly greater than those of SCC subtype [(57.0 ± 27.2)] (p-value = 0.02). However, two central SCCs undergoing artefacts from vena cava and pulmonary artery have an artificially increased mean BF.

Conclusions

The different hemodynamic behaviour of AC and SCC should be considered as a biomarker supporting treatment planning to select the patients, mainly with AC, that would most benefit from antiangiogenic therapies. The significance of results was achieved by automatically detecting and excluding artefactual BF values.