Peritoneal metastasis: detection with 16- or 64-detector row CT in patients undergoing surgery for gastric cancer

Cross-sectional imaging of gastric cancer: pearls, pitfalls and lessons learned from multidisciplinary conference

Gastric cancer is rising in prevalence associated with high mortality, primarily due to late-stage detection, underscoring the imperative for early and precise diagnosis. Etiology involves an interplay of genetic susceptibilities and environmental factors with a prominent role of Helicobacter pylori infection. Due to its often-delayed symptom presentation, prompt and accurate diagnosis is necessary. A multimodal imaging approach, including endoscopic ultrasound (EUS), multi-detector computed tomography (MDCT), and magnetic resonance imaging (MRI) is critical for accurate staging. Each modality contributes unique advantages and limitations, highlighting the importance of integrating diagnostic strategy. Moreover, multidisciplinary conferences offer a vital collaborative platform, bringing together specialists from diverse fields for treatment planning. This synergistic approach not only enhances diagnostic precision but also improves patient outcome. This review highlights the critical role of imaging in diagnosis, staging, and management and advocates for interdisciplinary collaboration in early detection and comprehensive management of gastric cancer, aiming to reduce mortality.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma

BACKGROUND

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment.

METHODS

This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts.

RESULTS

Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model.

CONCLUSIONS

The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

CT-Based Radiomics for the Preoperative Prediction of Occult Peritoneal Metastasis in Epithelial Ovarian Cancers

RATIONALE AND OBJECTIVES

The objective of this study was to develop a comprehensive combined model for predicting occult peritoneal metastasis (OPM) in epithelial ovarian cancers (EOCs) using radiomics features derived from computed tomography (CT) and clinical-radiological predictors.

MATERIALS AND METHODS

A total of 224 patients with EOCs were randomly divided into training dataset (N = 156) and test dataset (N = 86). Five clinical factors and seven radiological features were collected. The radiomics features were extracted from CT images of each patient. Multivariate logistic regression was employed to construct clinical and radiological models. The correlation analysis and least absolute shrinkage and selection operator algorithm were used to select radiomics features and build radiomics model. The important clinical, radiological factors, and radiomics features were integrated into a combined model by multivariate logistic regression. Receiver operating characteristics curve with area under the curve (AUC) were used to evaluate and compare predictive performance.

RESULTS

Carbohydrate antigen 125 (CA-125) and human epididymal protein 4 (HE-4) were independent clinical predictors. Laterality, thickened septa and margin were independent radiological predictors. In the training dataset, the AUCs for the clinical, radiological and radiomics models in evaluating OPM were 0.759, 0.819, and 0.830, respectively. In the test dataset, the AUCs for these models were 0.846, 0.835, and 0.779, respectively. The combined model outperformed other models in both the training and the test datasets with AUCs of 0.901 and 0.912, respectively. Decision curve analysis indicated that the combined model yielded a higher net benefit compared to the other models.

CONCLUSION

The combined model, integrating radiomics features with clinical and radiological predictors exhibited improved accuracy in predicting OPM in EOCs.

Fluorescence-Guided Laparoscopy after Oral Hypericin Administration for Staging of Locally Advanced Gastric Cancer-A Pilot Study

(1) Background: Laparoscopic staging is essential in gastric cancer (GC) to rule out peritoneal metastasis (PM). Hypericin, a plant-derived fluorescent compound, has been suggested to improve laparoscopic visualization of PM from GC. This prospective, single-arm, open-label clinical trial aimed to assess the feasibility and safety of oral hypericin administration as well as the suitability of fluorescence-guided laparoscopy (FGL) for improving the sensitivity and specificity of staging in GC patients (EudraCT-Number: 2015-005277-21; clinicaltrials.gov identifier: NCT-02840331). (2) Methods: GC patients received Laif® 900, an approved hypericin-containing phytopharmaceutical, once orally two to four hours before white light and ultraviolet light laparoscopy. The peritoneal cancer index was evaluated, biopsies taken and hypericin concentrations in serum and peritoneal tissue were determined by mass spectrometry. (3) Results: Between 2017 and 2021, out of 63 patients screened for eligibility, 50 patients were enrolled and treated per protocol. The study intervention was shown to be feasible and safe in all patients. Standard laparoscopy revealed suspicious lesions in 27 patients (54%), among whom 16 (59%) were diagnosed with PM. FGL identified suspicious areas in 25 patients (50%), among whom PM was confirmed in 13 cases (52%). Although hypericin concentrations in serum reached up to 5.64 ng/mL, no hypericin was detectable in peritoneal tissue biopsies. (4) Conclusions: FGL in patients with GC was shown to be feasible but futile in this study. Sufficient levels of hypericin should be ensured in target tissue prior to reassessing FGL with hypericin.

68Ga-FAPI-04 positron emission tomography/CT and laparoscopy for the diagnosis of occult peritoneal metastasis in newly diagnosed locally advanced gastric cancer: study protocol of a single-centre prospective cohort study

INTRODUCTION

Accurate baseline clinical staging is critical to inform treatment decision-making for patients with gastric cancers. Peritoneal metastasis (PM) is the most common form of metastasis in gastric cancer and mainly diagnosed by diagnostic laparoscopy and peritoneal lavage evaluation. However, diagnostic laparoscopy is invasive and less cost-effective. It is urgent to develop a safe, fast and non-invasive functional imaging method to verify the peritoneal metastasis of gastric cancer. The aim of our study was to evaluate the proportion of patients in whom 68Ga-FAPI-04 positron emission tomography/CT (PET/CT) led to a change in treatment strategy and to assess the diagnostic accuracy of 68Ga-FAPI-04 PET/CT for the detection of occult peritoneal metastasis compared with laparoscopic exploration.

METHODS AND ANALYSIS

In this single-centre, prospective diagnostic test accuracy study, a total of 48 patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma (cT4a-b, N0-3, M0, based on CT images) who are considering radical tumour surgery will be recruited. All participants will undergo 68Ga-FAPI-04 PET/CT before the initiation of laparoscopic exploration. The primary outcome is the proportion of patients with occult peritoneal metastatic lesions detected by 68Ga-FAPI-04 PET/CT, leading to a change in therapy strategy. The secondary outcomes include the diagnostic performance of 68Ga-FAPI-04 PET/CT for occult peritoneal metastasis, including sensitivity, specificity, accuracy, positive predictive value and negative predictive value.

ETHICS AND DISSEMINATION

The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (2022-1484). Study results will be presented at public and scientific conferences and in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ChiCTR2300067591.

Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis

Peritoneal carcinomatosis (PC) is a type of secondary cancer which is not sensitive to conventional intravenous chemotherapy. Treatment strategies for PC are usually palliative rather than curative. Recently, artificial intelligence (AI) has been widely used in the medical field, making the early diagnosis, individualized treatment, and accurate prognostic evaluation of various cancers, including mediastinal malignancies, colorectal cancer, lung cancer more feasible. As a branch of computer science, AI specializes in image recognition, speech recognition, automatic large-scale data extraction and output. AI technologies have also made breakthrough progress in the field of peritoneal carcinomatosis (PC) based on its powerful learning capacity and efficient computational power. AI has been successfully applied in various approaches in PC diagnosis, including imaging, blood tests, proteomics, and pathological diagnosis. Due to the automatic extraction function of the convolutional neural network and the learning model based on machine learning algorithms, AI-assisted diagnosis types are associated with a higher accuracy rate compared to conventional diagnosis methods. In addition, AI is also used in the treatment of peritoneal cancer, including surgical resection, intraperitoneal chemotherapy, systemic chemotherapy, which significantly improves the survival of patients with PC. In particular, the recurrence prediction and emotion evaluation of PC patients are also combined with AI technology, further improving the quality of life of patients. Here we have comprehensively reviewed and summarized the latest developments in the application of AI in PC, helping oncologists to comprehensively diagnose PC and provide more precise treatment strategies for patients with PC.

Prediction of occult peritoneal metastases or positive cytology using CT in gastric cancer

OBJECTIVE

Accurate prediction of preoperative occult peritoneal metastasis (OPM) is critical to selecting appropriate therapeutic regimen for gastric cancer (GC). Considering the clinical practicability, we develop and validate a visible nomogram that integrates the CT images and clinicopathological parameters for the individual preoperative prediction of OPM in GC.

METHODS

This retrospective study included 520 patients who underwent staged laparoscopic exploration or peritoneal lavage cytology (PLC) examination. Univariate and multivariate logistic regression results were used to screen model predictors and construct nomograms of OPM risk. The performance of the model was detected by using ROC, accuracy, and C-index. The bootstrap resampling method was considered internal validation of the model. The Delong test was used to evaluate the difference in AUC between the two models.

RESULTS

Grade 2 mural stratification, tumor thickness, and the Lauren classification diffuse were significant predictors of OPM (p < 0.05). The nomogram of these three factors (compared with the original model) showed a higher predictive effect (p < 0.001). The area under the curve (AUC) of the model was 0.830 (95% CI 0.788-0.873), and the internally validated AUC of 1000 bootstrap samples was 0.826 (95% CI 0.756-0.870). The sensitivity, specificity, and accuracy were 76.0%, 78.8%, and 78.3%, respectively.

CONCLUSIONS

CT phenotype-based nomogram demonstrates favorable discrimination and calibration, and it can be conveniently used for preoperative individual risk rating of OPM in GC.

CLINICAL RELEVANCE STATEMENT

In this study, the preoperative OPM prediction model based on CT images (mural stratification, tumor thickness) combined with pathological parameters (the Lauren classification) showed excellent predictive ability in GC, and it is also suitable for clinicians to use rather than limited to professional radiologists.

KEY POINTS

• Nomogram based on CT image analysis can effectively predict occult peritoneal metastasis in gastric cancer (training area under the curve (AUC) = 0.830 and bootstrap AUC = 0.826). • Nomogram model combined with CT features performed better than the original model (established using only clinicopathological parameters) in differentiating occult peritoneal metastasis of gastric cancer.

A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer

PURPOSE

Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.

METHOD

In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.

RESULT

Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P  < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.

CONCLUSION

The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

Systematic review of diagnostic tools for peritoneal metastasis in gastric cancer-staging laparoscopy and its alternatives

BACKGROUND

Gastric cancer is one of the leading causes of cancer burden and mortality, often resulting in peritoneal metastasis in advanced stages with negative survival outcomes. Staging laparoscopy has become standard practice for suspected cases before a definitive gastrectomy or palliation. This systematic review aims to compare the efficacy of other diagnostic modalities instead of staging laparoscopy as the alternatives are able to reduce cost and invasive staging procedures. Recently, a radiomic model based on computed tomography and positron emission tomography (PET) has also emerged as another method to predict peritoneal metastasis.

AIM

To determine if the efficacy of computed tomography, magnetic resonance imaging and PET is comparable with staging laparoscopy.

METHODS

Articles comparing computed tomography, PET, magnetic resonance imaging, and radiomic models based on computed tomography and PET to staging laparoscopies were filtered out from the Cochrane Library, EMBASE, PubMed, Web of Science, and Reference Citations Analysis (https://www.referencecitationanalysis.com/). In the search for studies comparing computed tomography (CT) to staging laparoscopy, five retrospective studies and three prospective studies were found. Similarly, five retrospective studies and two prospective studies were also included for papers comparing CT to PET scans. Only one retrospective study and one prospective study were found to be suitable for papers comparing CT to magnetic resonance imaging scans.

RESULTS

Staging laparoscopy outperformed computed tomography in all measured aspects, namely sensitivity, specificity, positive predictive value and negative predictive value. Magnetic resonance imaging and PET produced mixed results, with the former shown to be only marginally better than computed tomography. CT performed slightly better than PET in most measured domains, except in specificity and true negative rates. We speculate that this may be due to the limited F-fluorodeoxyglucose uptake in small peritoneal metastases and in linitis plastica. Radiomic modelling, in its current state, shows promise as an alternative for predicting peritoneal metastases. With further research, deep learning and radiomic modelling can be refined and potentially applied as a preoperative diagnostic tool to reduce the need for invasive staging laparoscopy.

CONCLUSION

Staging laparoscopy was superior in all measured aspects. However, associated risks and costs must be considered. Refinements in radiomic modelling are necessary to establish it as a reliable screening technique.

Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment

PURPOSE

The purpose of this meta-analysis is to systematically review the diagnostic performance of radiomic techniques in predicting peritoneal metastasis in patients with gastric cancer, and to evaluate the quality of current research.

METHODS

We searched PubMed, Web of Science, EBSCO, Embase, and Cochrane databases for relevant studies up to April 3, 2023. Data extraction and quality evaluation were performed by two independent reviewers. Then we performed statistical analysis, including plotting the forest plot and summary receiver operating characteristic (SROC) curve, and source of heterogeneity analysis, through the MIDAS module in Stata 15. We performed meta-regression and subgroup analyses to analyze the sources of heterogeneity. Using the QUADAS-2 scale and the RQS scale to assess the quality of retrieved studies.

RESULTS

Ten studies with 6199 patients were finally included in our meta-analysis. Pooled sensitivity and specificity were 0.77 (95% confidence interval [CI]: 0.66, 0.86), and 0.88 (95% CI 0.80, 0.93), respectively. The overall AUC was 0.89 (95% CI 0.86, 0.92). The heterogeneity of this meta-analysis was high, with I2 = 88% (95% CI 75,100). The result of meta-regression showed that QUADAS-2 results, RQS results and machine learning method led to heterogeneity in sensitivity and specificity (P < 0.05). Furthermore, the image segmentation area and the presence or absence of combined clinical factors were associated with sensitivity heterogeneity and specificity heterogeneity, respectively.

CONCLUSION

Undoubtedly, radiomics has potential value in diagnosing peritoneal metastasis of gastric cancer, but the quality of current research is inconsistent, and more standardized and high-quality research is still needed in the future to achieve the transformation of radiomics results into clinical applications.

The Role of Additional Staining in the Assessment of the Peritoneal Regression Grading Score (PRGS) in Peritoneal Metastasis of Gastric Origin

INTRODUCTION

The Peritoneal Regression Grading Score (PRGS) is a 4-tied histologic regression grading score for determining the response of peritoneal metastasis to chemotherapy. Peritoneal biopsies in every abdominal quadrant are recommended. A positive therapy response is defined as a decreasing or stable mean PRGS between 2 therapy cycles. The added value of periodic acid satin (PAS) and Ber-EP4 staining over HE staining for diagnosing PRGS1 (the absence of vital tumor cells) is unclear.

MATERIALS AND METHODS

A total of 339 biopsies obtained during 76 laparoscopies in 33 patients with peritoneal metastasis of gastric cancer were analyzed. Biopsies classified as PRGS 1 (no residual tumor, n=95) or indefinite (n=50) were stained with PAS, and remaining indefinite or PRGS1 cases additionally stained with BerEP4.

RESULTS

After PAS-staining tumor cells were detected in 28 out of 145 biopsies (19%), the remaining 117 biopsies were immunostained with Ber-EP4. Tumor cells were detected in 22 biopsies (19%). In total, additional staining allowed the detection of residual tumor cells in 50 out of 339 biopsies (15%) and changed the therapy response assessment in 7 out of 33 (21%) patients.

CONCLUSIONS

In summary, 25% (24 out of 95) of initially tumor-free samples (PRGS1) showed residual tumor cells after additional staining with PAS and/or BerEp4. Immunohistochemistry provided important additional information (the presence of tumor cells) in 22 of all 339 biopsies (11.2%). Further staining reduced the instances of unclear diagnosis from 50 to 0 and changed the therapy response assessment in 7 out of 33 patients (21%). We recommend additional staining in PRGS1 or unclear cases.

Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer

Background

The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Methods

The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability.

Results

The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility.

Conclusion

CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

Can visceral fat parameters based on computed tomography be used to predict occult peritoneal metastasis in gastric cancer?

BACKGROUND

The preoperative prediction of peritoneal metastasis (PM) in gastric cancer would prevent unnecessary surgery and promptly indicate an appropriate treatment plan.

AIM

To explore the predictive value of visceral fat (VF) parameters obtained from preoperative computed tomography (CT) images for occult PM and to develop an individualized model for predicting occult PM in patients with gastric carcinoma (GC).

METHODS

A total of 128 confirmed GC cases (84 male and 44 female patients) that underwent CT scans were analyzed and categorized into PM-positive (n = 43) and PM-negative (n = 85) groups. The clinical characteristics and VF parameters of two regions of interest (ROIs) were collected. Univariate and stratified analyses based on VF volume were performed to screen for predictive characteristics for occult PM. Prediction models with and without VF parameters were established by multivariable logistic regression analysis.

RESULTS

The mean attenuations of VF_{ROI 1} and VF_{ROI 2} varied significantly between the PM-positive and PM-negative groups (P = 0.044 and 0.001, respectively). The areas under the receiver operating characteristic curves (AUCs) of VF_{ROI 1} and VF_{ROI 2} were 0.599 and 0.657, respectively. The mean attenuation of VF_{ROI 2} was included in the final prediction combined model, but not an independent risk factor of PM (P = 0.068). No significant difference was observed between the models with and without mean attenuation of VF (AUC: 0.749 vs 0.730, P = 0.339).

CONCLUSION

The mean attenuation of VF is a potential auxiliary parameter for predicting occult PM in patients with GC.

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 characteristics for predicting prognosis of gastric cancer with synchronous peritoneal metastasis

Introduction

To explore the CT characteristics for the prediction of long term survival in gastric cancer patients with synchronous peritoneal metastasis (PM).

Materials and methods

Sixty-six patients diagnosed as gastric cancer with synchronous peritoneum metastasis were enrolled in this retrospective study. Ten anatomic peritoneal regions were evaluated to check for the signs of PM on CT. One positive area equaled one score. The CT characteristic-based PM score (CT-PMS) was the sum of the total points assigned to all 10 regions, with a range of 0-10. The triple tract dilatation (TTD) sign caused by peritoneal metastasis, the presence of extensive lymph node metastasis (ELM), and the grade of ascites were recorded. The overall survival (OS) was used as the prognostic indicator. The performance of the CT characteristics was assessed by the Kaplan-Meier analysis and Cox proportional hazards model, while its reproducibility was evaluated by Kappa statistic and weighted Kappa statistic.

Results

Patients with a CT-PMS of 3-10 had significantly poorer OS (P = .02). Patients with either the presence of TTD sign, or ELM had a trend toward unfavorable OS (both P = .07), and when CT-PMS of 3-10 was detected simultaneously, the survival was further reduced (P = .00 for TTD sign; P = .01 for ELM). The grade of ascites failed to show a significant correlation with OS. The interobserver reproducibility for assessing the CT-PMS, the presence of TTD sign, the presence of ELM, and the grade of ascites had a substantial to almost perfect agreement.

Conclusion

The prognosis of gastric cancer patients with PM has a correlation with the extent of metastasis dissemination on baseline CT. A CT-PMS of 3-10 is associated with a worse prognosis than that of 0-2. The presence of TTD sign and ELM may help further select patients with extraordinarily poor prognoses.

Korean Practice Guidelines for Gastric Cancer 2022: An Evidence-based, Multidisciplinary Approach

Gastric cancer is one of the most common cancers in Korea and the world. Since 2004, this is the 4th gastric cancer guideline published in Korea which is the revised version of previous evidence-based approach in 2018. Current guideline is a collaborative work of the interdisciplinary working group including experts in the field of gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology and guideline development methodology. Total of 33 key questions were updated or proposed after a collaborative review by the working group and 40 statements were developed according to the systematic review using the MEDLINE, Embase, Cochrane Library and KoreaMed database. The level of evidence and the grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation proposition. Evidence level, benefit, harm, and clinical applicability was considered as the significant factors for recommendation. The working group reviewed recommendations and discussed for consensus. In the earlier part, general consideration discusses screening, diagnosis and staging of endoscopy, pathology, radiology, and nuclear medicine. Flowchart is depicted with statements which is supported by meta-analysis and references. Since clinical trial and systematic review was not suitable for postoperative oncologic and nutritional follow-up, working group agreed to conduct a nationwide survey investigating the clinical practice of all tertiary or general hospitals in Korea. The purpose of this survey was to provide baseline information on follow up. Herein we present a multidisciplinary-evidence based gastric cancer guideline.

Risk factors associated with peritoneal carcinomatosis of gastric cancer in staging laparoscopy: A systematic review and meta-analysis

Background

The optimal indications of staging laparoscopy in gastric cancer to detect peritoneal carcinomatosis are still controversial. We performed this systematic review and meta-analysis to quantify the relevance of the preoperative factors with peritoneal carcinomatosis to explore the indications of staging laparoscopy.

Materials and methods

Systematic searches were conducted using Medline, Embase, and the Cochrane Library in December 2021. On the basis of calculating the odds ratio (OR) of each factor, we quantified the association between the risk factors and peritoneal carcinomatosis such as clinical T/N stage, Borrmann type, and tumor markers, using meta-analysis with a random-effects model.

Results

A total of 21 case-control studies and one cohort study were obtained. T stage, N stage, and differentiation degree were most widely studied, with OR values of 2.96 (95% CI: 1.87–4.69), 1.22 (95% CI: 0.86–1.73), and 1.91 (95% CI: 1.42–2.56), respectively. Among all the factors, elevated CA125 (OR = 19.45, 95% CI: 4.71–80.30), Borrmann type IV (OR = 7.68, 95% CI: 3.62–16.27), and large tumor diameter (OR = 5.12, 95% CI: 2.55–10.31) had the highest OR. In particular, CA125 had the best predictability for peritoneal carcinomatosis but was only mentioned by three articles.

Conclusions

There was a cognitive gap between the awareness and importance of risk factors for peritoneal carcinomatosis. In addition to T4 stage, patients with factors with high OR, such as Borrmann type IV, large tumor diameter, and elevated CA125, should undergo staging laparoscopy.

Predicting peritoneal carcinomatosis of gastric cancer: A simple model to exempt low-risk patients from unnecessary staging laparoscopy

Background

Peritoneal carcinomatosis (PC) of gastric cancer indicates a poor outcome and is mainly diagnosed by staging laparoscopy (SL). This study was designed to develop a risk stratification model based on the number of risk factors to exempt low-risk patients from unnecessary SL.

Methods

This was a retrospective cohort study based on a single institution between January 2015 and December 2019. SL is indicated for patients of advanced locoregional stage, and clinicopathologic characteristics of 535 consecutive patients were included. PC-associated variables were identified by logistic regression analysis. A risk stratification model based on the number of risk factors was constructed, and we defined its predictive value with a receiver operating characteristic (ROC) curve and negative predictive value.

Results

In total, 15.9% of included patients were found to have PC during SL. Borrmann type IV, elevated CA125, and tumour diameter ≥5 cm were independent risk factors of PC. These three factors combined with cT4 were selected as predictive factors, and the number of predictive variables was significantly related to the possibility of PC (2.0%, 12.8%, 20.0%, 54.2%, and 100%, respectively). When the cutoff value is more than one predictive factor, the negative predictive value is 98.0%, with an area under the curve of 0.780. This model could exempt 29.8% of unnecessary SL compared to the indication of the current NCCN guideline.

Conclusions

We constructed a simple model to predict the probability of PC using the number of predictive factors. It is recommended that patients without any of these factors should be exempt from SL.

Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer

BACKGROUND

Occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients remains a major diagnostic challenge. The aim of this study was to develop novel predictive models for identification of OPM in AGCs.

METHOD

A total of 810 patients with primary AGCs from two hospitals were retrospectively selected and divided into training (n = 393), internal validation (n = 215) and external validation cohorts (n = 202). CT based machine learning models were built and tested to predict the OPM status in AGCs., which are 1) Radiomic signatures: using venous CT imaging features, 2) Clinical models: integrating tumor location, differentiation and extent of serosal exposure, and 3) Radiomics models: combining of radiomic signature, tumor location and tumor differentiation.

RESULT

Total incidence of OPM was 8.27% (67/810). Clinical models yielded comparable classification accuracy with the corresponding radiomics models with similar AUCs (0.902-0.969 vs. 0.896-0.975) while the radiomic signatures showed relatively low AUCs of 0.863-0.976. In the case where the specificity is higher than 90%, the overall sensitivity of clinical model and radiomics model for OPM positive cases was 76.1% (51/67) and 82.1% (55/67). A nomogram based on the logistic clinical model was drawn to facilitate the usage and verification of the clinical model.

CONCLUSION

Both the novel CT based clinical nomogram and radiomics model provide promising method to yield high accuracy in identification of OPM in AGC patients.

Construction of a Nomogram Model for Predicting Peritoneal Dissemination in Gastric Cancer Based on Clinicopathologic Features and Preoperative Serum Tumor Markers

Background

Peritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients.

Methods

In our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people’s hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots.

Results

Univariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p<0.0006), depth of invasion (OR=14.55, p<0.0001), lymph node metastases (OR=5.89, p<0.0001), carcinoembryonic antigen (CEA) (OR=2.50, p<0.0001), CA125 (OR=11.46, p<0.0001), CA72-4 (OR=4.09, p<0.0001), CA19-9 (OR=2.74, p<0.0001), CA50 (OR=5.20, p<0.0001) and CA242 (OR=3.83, p<0.0001). Multivariate analysis revealed that clinical invasion depth and serum marker of CA125 and CA72-4 were independent risk factors for PD. The prediction model was established based on the risk factors using the R program. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.931 (95% CI: 0.900–0.960), with the accuracy, sensitivity and specificity values of 90.5%, 86.2% and 82.2%, respectively.

Conclusion

The nomogram model constructed using CA125, CA72-4 and depth of invasion increases the accuracy and sensitivity in predicting the incidence of PD in GC patients and can be used as an important tool for preoperative diagnosis.

Integration of Genomic Biology Into Therapeutic Strategies of Gastric Cancer Peritoneal Metastasis

The peritoneum is a common site of metastasis in advanced gastric cancer (GC). Diagnostic laparoscopy is now routinely performed as part of disease staging, leading to an earlier diagnosis of synchronous peritoneal metastasis (PM). The biology of GCPM is unique and aggressive, leading to a dismal prognosis. These tumors tend to be resistant to traditional systemic therapy, and yet, this remains the current standard-of-care recommended by most international clinical guidelines. As this is an area of unmet clinical need, several translational studies and clinical trials have focused on addressing this specific disease state. Advances in genomic sequencing and molecular profiling have revealed several promising therapeutic targets and elucidated novel biology, particularly on the role of the surrounding tumor microenvironment in GCPM. Peritoneal-specific clinical trials are being designed with a combination of locoregional therapeutic strategies with systemic therapy. In this review, we summarize the new knowledge of cancer biology, advances in surgical techniques, and emergence of novel therapies as an integrated strategy emerges to address GCPM as a distinct clinical entity.

Preoperative Folate Receptor-Positive Circulating Tumor Cells Are Associated With Occult Peritoneal Metastasis and Early Recurrence in Gastric Cancer Patients: A Prospective Cohort Study

Background

The aim of this study is to explore the clinical feasibility of detecting folate receptor-positive circulating tumor cells (FR+ CTCs) for predicting peritoneal metastasis and short-term outcome in gastric cancer patients.

Methods

This is a prospective, single-center, observational study. We applied ligand-targeted enzyme-linked polymerization method to detect preoperative FR+ CTC levels in peripheral blood. We evaluated the diagnostic value of FR+ CTCs and other biomarkers in predicting peritoneal metastasis. Prognostic factors for recurrence-free survival (RFS) were investigated in univariate and multivariate analyses.

Results

A total of 132 patients with gastric cancer and 9 patients with benign disease were recruited. Gastric cancer patients had a significantly higher CTC level compared to that of patients with benign disease (p < 0.01). Combined model including CTC level and other biomarkers presented high sensitivity (100%) and moderate specificity (59.3%) in predicting peritoneal metastasis. Univariate analysis revealed that decreased serum prealbumin, decreased peripheral lymphocyte count, FR+ CTCs, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and lymph node metastasis were significantly associated with shorter RFS. FR+ CTC level [≥12.6 folate units (FU)/3 ml, hazard ratio (HR) = 6.957, p = 0.005] and CA19-9 (>34 ng/ml, HR = 3.855, p = 0.037) were independent prognostic factors in multivariate analysis.

Conclusions

Our findings for the first time suggested the diagnostic value of preoperative CTC levels in predicting peritoneal metastasis in gastric cancer. Moreover, the FR+ CTC level could be a novel and promising prognostic factor for the recurrence of gastric cancer in patients who underwent surgery.

Clinical Trial Registration

Chinese Clinic Trial Registry, identifier ChiCTR2100050514.

Assessment of the use of computed tomography colonography in early detection of peritoneal metastasis in patients with gastric cancer: A prospective cohort study

Peritoneal metastasis (PM) is one of the most frequent forms of gastric cancer recurrence. In this study, we aimed to use computed tomography (CT) colonography (CTC) to detect signs of PM earlier in patients in whom PM was suspected but not yet diagnosed. CTC was used to evaluate patients with clinical symptoms or general CT findings that were suspicious but not sufficient to confirm PM. In total, 18 patients with suspected PM were enrolled. Ten patients (55.6%) had PM on CTC. Abnormal colonic deformities were identified at locations other than those of the lesions detected by general CT in seven patients. The sensitivity and specificity of CTC for the detection of PM were 83.3% and 100%, respectively. The median overall survival after CTC was 201 days in the CTC-positive group, which was significantly shorter than that in the CTC-negative group (945 days, p = 0.01). In the multivariate analysis, a positive CTC finding was the only factor independently associated with survival (p = 0.005). According to our experience with 18 patients, CTC can be an alternative to conventional imaging for early detection of PM. Further prospective studies with larger sample sizes are warranted to confirm and validate these findings. University hospital Medical Information Network Clinical Trials Registry (UMIN-CTR): Registration number: UMIN000044167.

Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer

PURPOSE

To compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC).

METHODS

In this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018. Clinical information and CT semantic features were collected, and CT radiomics features were extracted. A predictive clinical-CT model was created using multivariate logistic regression. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. These models were validated with an external cohort (n = 30). Receiver operating characteristics curve with area under the curve (AUC), sensitivity, and specificity were used to evaluate predictive performance.

RESULTS

Tumor size, mild ascites, and serum CA125 were independent factors predictive of occult PM. The clinical-CT model of these independent factors showed better diagnostic performance than 2D and 3D radiomics models. In the external validation cohort, the AUCs of different models were as follows-clinical-CT model: 0.853 (sensitivity, 66.7%; specificity, 93.3%); 2D radiomics model: 0.622 (sensitivity, 80.0%; specificity, 46.7%); and 3D radiomics model: 0.676 (sensitivity, 60.0%; specificity, 86.0%). The clinical-CT model nomogram showed good clinical predictive efficiency to assess occult PM.

CONCLUSION

The clinical-CT model was better than the radiomics models in predicting occult PM in AGC.

A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study

Purpose

To develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.

Materials and Methods

599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.

Results

The M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.

Conclusions

The BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

Three-year outcomes of the randomized phase III SEIPLUS trial of extensive intraoperative peritoneal lavage for locally advanced gastric cancer

Whether extensive intraoperative peritoneal lavage (EIPL) after gastrectomy is beneficial to patients with locally advanced gastric cancer (AGC) is not clear. This phase 3, multicenter, parallel-group, prospective randomized study (NCT02745509) recruits patients between April 2016 and November 2017. Eligible patients who had been histologically proven AGC with T3/4NxM0 stage are randomly assigned (1:1) to either surgery alone or surgery plus EIPL. The results of the two groups are analyzed in the intent-to-treat population. A total of 662 patients with AGC (329 patients in the surgery alone group, and 333 in the surgery plus EIPL group) are included in the study. The primary endpoint is 3-year overall survival (OS). The secondary endpoints include 3-year disease free survival (DFS), 3-year peritoneal recurrence-free survival (reported in this manuscript) and 30-day postoperative complication and mortality (previously reported). The trial meets pre-specified endpoints. Estimated 3-year OS rates are 68.5% in the surgery alone group and 70.6% in the surgery plus EIPL group (log-rank p = 0.77). 3-year DFS rates are 61.2% in the surgery alone group and 66.0% in the surgery plus EIPL group (log-rank p = 0.24). The pattern of disease recurrence is similar in the two groups. In conclusion, EIPL does not improve the 3-year survival rate in AGC patients.

Imaging of Gastric Carcinomatosis

Diagnosing the absence or presence of peritoneal carcinomatosis in patients with gastric cancer, including its extent and distribution, is an essential step in patients' therapeutic management. Such diagnosis still remains a radiological challenge. In this article, we review the strengths and weaknesses of the different imaging techniques for the diagnosis of peritoneal carcinomatosis of gastric origin as well as the techniques' imaging features. We also discuss the assessment of response to treatment and present recommendations for the follow-up of patients with complete surgical resection according to the presence of risk factors of recurrence, as well as discussing future directions for imaging improvement.

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

Objectives

The aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC).

Methods

In this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (¹⁸F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets.

Results

After dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram.

Conclusions

The comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.

Adrenal Nodules Detected at Staging CT in Patients with Resectable Gastric Cancers Have a Low Incidence of Malignancy

Background Guidelines recommending additional imaging for adrenal nodules lack relevant epidemiologic evidence. Purpose To measure the prevalence of adrenal nodules detected at staging CT in patients with potentially resectable gastric cancer and the proportion of patients with malignant nodules among them. Materials and Methods This retrospective study included 10 250 consecutive patients (median age, 63 years; interquartile range, 53-71 years; 6884 men) who underwent staging CT and had potentially resectable gastric cancer in a tertiary center (May 2003 to December 2018). All 10 250 CT studies were retrospectively reviewed, and patients with adrenal nodules (or thickening ≥10 mm) were identified to measure the prevalence of adrenal nodules. Among patients with adrenal nodules, the per-patient proportions of malignant nodules, adrenal metastasis from gastric cancer, and additional adrenal examinations were measured. A secondary analysis was performed by using data from the original CT reports. The same metrics that were used in the retrospective review were assessed. Results The prevalence of adrenal nodules was 4.5% (95% CI: 4.1, 4.9; 462 of 10 250). The proportions of malignant nodules and adrenal metastasis from gastric cancer were 0.4% ( 95% CI: 0.1, 1.6; two of 462) and 0% (95% CI: 0.0, 0.8; 0 of 462), respectively. A total of 27% of the patients (95% CI: 23, 31; 123 of 462) underwent additional adrenal examination. According to original CT reports, the prevalence of adrenal nodules and the proportions of malignant nodules, adrenal metastases from gastric cancer, and additional adrenal examination were 2.7% (95% CI: 2.4, 3.0; 272 of 10 250), 0.7% (95% CI: 0.1, 2.6; two of 272), 0% (95% CI: 0.0, 1.4; 0 of 272), and 42.6% (95% CI: 36.7, 48.8; 116 of 272), respectively. Conclusion Although adrenal nodules were detected frequently on staging CT images of patients with otherwise resectable gastric cancer, these nodules were rarely malignant. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Baumgarten in this issue.

Does Extended Intraoperative Peritoneal Lavage Really Bring Benefit on Patients With Gastric Cancer? A Meta-Analysis of Published Clinical Trials

Purpose

The purpose of the current meta-analysis is to analyze whether extended intraoperative peritoneal lavage (EIPL) can bring benefit on short-term outcomes or survival for patients undergoing curative gastrectomy for gastric cancer.

Methods

The PubMed, Embase, and Cochrane Library databases were searched from inception to May 3, 2021, to find eligible studies. Postoperative complications, overall survival (OS), disease-free survival (DFS), and peritoneal recurrence-free survival (PRFS) were compared between EIPL group and No EIPL group.

Results

A total of five randomized controlled trials with 1,790 patients were included in the current meta-analysis. No difference was found in baseline information (p > 0.05). After pooling up the data of overall postoperative complications, no significant difference was found between EIPL group and No EIPL group (OR = 0.88, 95% CI = 0.51 to 1.53, P = 0.65). Furthermore, there was no significant difference between EIPL group and No EIPL group in terms of OS (HR = 0.77, 95% CI = 0.36 to 1.64, P = 0.49), DFS (HR = 0.97, 95% CI = 0.71 to 1.33, P = 0.87), and PRFS (HR = 1.03, 95% CI = 0.74 to 1.43, P = 0.86). In terms of subgroup analysis of OS, no significant difference was found as well (HR = 1.05, 95% CI = 0.82 to 1.34, P = 0.69).

Conclusions

EIPL did not bring benefit in terms of short-term outcomes or survival. Therefore, EIPL is not recommended for patients undergoing curative gastrectomy for gastric cancer.

A qualitative transcriptional signature of recurrence risk for stages II-III gastric cancer patients after surgical resection

BACKGROUND AND AIM

Metastasis is the leading cause of recurrence in gastric cancer. However, the imaging techniques and pathological examinations for tumor metastasis have a high false-positive rate or a high false-negative rate, and many proposed that metastasis-related molecular biomarkers can hardly be validated in independent datasets.

METHODS

We propose to use significantly stable gene pairs with reversal relative expression orderings (REOs) between non-metastasis and metastasis gastric cancer samples as the metastasis-related gene pairs. Based on the REOs of these gene pairs, we developed a qualitative transcriptional signature for predicting the recurrence risk of stages II-III gastric cancer patients after surgical resection.

RESULTS

A REOs-based signature, consisting of 19 gene pairs (19-GPS), was selected from 77 stages II-III gastric cancer patients and validated in two independent datasets. Samples in the high-risk group had shorter disease-free survival time and overall survival time than those in the low-risk group. Differentially expressed genes (DEGs) between the high- and low-risk groups classified by 19-GPS were highly reproducible comparing with those between lymph node metastasis and lymph node non-metastasis groups. Functional enrichment analysis showed that these DEGs were significantly enriched in metastasis-related pathways, such as PI3K-Akt and Rap1 signaling pathways. The multi-omics analyses suggested that the epigenetic and genomic features might cause transcriptional differences between two subgroups, which help to characterize the mechanism of gastric cancer metastasis.

CONCLUSIONS

The signature could robustly identify patients at high recurrence risk after resection surgery, and the multi-omics analyses might aid in revealing the metastasis-related characteristics of gastric cancer.

Prevalence and Clinical Implications of Ascites in Gastric Cancer Patients after Curative Surgery

We aimed to determine the frequency and clinical significance of ascites that developed during the follow-up period in patients who underwent curative resection for gastric cancer. The study included 577 patients with gastric cancer who underwent curative gastrectomy. Among them, 184 showed ascites in postoperative follow-up images. Benign ascites was observed in 131 of 490 patients without recurrence, 48 patients (of 87) with recurrence had malignancy-related ascites, and the remaining 5 patients had ascites only prior to recurrence. In most patients without recurrence (97.7%) and in 50% of patients with malignancy-related ascites, the ascites was small in volume and located in the pelvic cavity at the time that it was first identified. However, with the exception of nine patients, malignancy-related pelvic ascites occurred simultaneously or after obvious recurrence. Of those nine patients who had minimal pelvic ascites before obvious recurrence, only one had a clear association with a malignancy-related ascites. In the multivariate analysis, an age of ≤45 was the only independent risk factor for the occurrence of benign ascites. A small volume of pelvic ascites fluid is common in young gastric cancer patients who do not have recurrence after gastrectomy, regardless of sex. It is rare for ascites to be the first manifestation of recurrence.

Dual-Energy Computed Tomography-Based Radiomics to Predict Peritoneal Metastasis in Gastric Cancer

Objective

To develop and validate a dual-energy computed tomography (DECT) derived radiomics model to predict peritoneal metastasis (PM) in patients with gastric cancer (GC).

Methods

This retrospective study recruited 239 GC (non-PM = 174, PM = 65) patients with histopathological confirmation for peritoneal status from January 2015 to December 2019. All patients were randomly divided into a training cohort (n = 160) and a testing cohort (n = 79). Standardized iodine-uptake (IU) images and 120-kV-equivalent mixed images (simulating conventional CT images) from portal-venous and delayed phases were used for analysis. Two regions of interest (ROIs) including the peritoneal area and the primary tumor were independently delineated. Subsequently, 1691 and 1226 radiomics features were extracted from the peritoneal area and the primary tumor from IU and mixed images on each phase. Boruta and Spearman correlation analysis were used for feature selection. Three radiomics models were established, including the R_IU model for IU images, the R_MIX model for mixed images and the combined radiomics model (the R_comb model). Random forest was used to tune the optimal radiomics model. The performance of the clinical model and human experts to assess PM was also recorded.

Results

Fourteen and three radiomics features with low redundancy and high importance were extracted from the IU and mixed images, respectively. The R_IU model showed significantly better performance to predict PM than the R_MIX model in the training cohort (AUC, 0.981 vs. 0.917, p = 0.034). No improvement was observed in the R_comb model (AUC = 0.967). The R_IU model was the optimal radiomics model which showed no overfitting in the testing cohort (AUC = 0.967, p = 0.528). The R_IU model demonstrated significantly higher predictive value on peritoneal status than the clinical model and human experts in the testing cohort (AUC, 0.785, p = 0.005; AUC, 0.732, p <0.001, respectively).

Conclusion

DECT derived radiomics could serve as a non-invasive and easy-to-use biomarker to preoperatively predict PM for GC, providing opportunity for those patients to tailor appropriate treatment.

Current status and future perspectives on neoadjuvant therapy in gastric cancer

Gastric cancer, with high morbidity and mortality rates, is one of the most heterogeneous tumors. Radical gastrectomy and postoperative chemotherapy are the standard treatments. However, the safety and efficacy of neoadjuvant therapy (NAT) need to be confirmed by many trials before implementation, creating a bottleneck in development. Although clinical benefits of NAT have been observed, a series of problems remain to be solved. Before therapy, more contributing factors should be offered for choice in the intended population and ideal regimens. Enhanced computed tomography (CT) scanning is usually applied to evaluate effectiveness according to Response Evaluation Criteria in Solid Tumors (RECIST), yet CT scanning results sometimes differ from pathological responses. After NAT, the appropriate time for surgery is still empirically defined. Our review aims to discuss the abovementioned issues regarding NAT for GC, including indications, selection of regimens, lesion assessment and NAT-surgery interval time.

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes.

Transcriptomic Profiling Identifies a Risk Stratification Signature for Predicting Peritoneal Recurrence and Micrometastasis in Gastric Cancer

PURPOSE

Gastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis.

EXPERIMENTAL DESIGN

We performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.

RESULTS

Genome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).

CONCLUSIONS

Our novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.

Accuracy of whole-body diffusion-weighted MRI (WB-DWI/MRI) in diagnosis, staging and follow-up of gastric cancer, in comparison to CT: a pilot study

Background

Accurate staging of patients with gastric cancer is necessary for selection of the most appropriate and personalized therapy. Computed tomography (CT) is currently used as primary staging tool, being widely available with a relatively high accuracy for the detection of parenchymal metastases, but with low sensitivity for the detection of peritoneal metastases. Magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) has a very high contrast resolution, suggesting a higher diagnostic performance in the detection of small peritoneal lesions. The aim of this study was to retrospectively evaluate the added value of whole-body diffusion-weighted MRI (WB-DWI/MRI) to CT for detection of peritoneal carcinomatosis (PC) and distant metastases in the preoperative staging of gastric cancer.

Methods

This retrospective study included thirty-two patients with a suspicion of gastric cancer/recurrence, who underwent WB-DWI/MRI at 1.5 T, in addition to CT of thorax and abdomen. Images were evaluated by two experienced abdominal radiologists in consensus. Histopathology, laparoscopy and/or 1-year follow-up were used as reference standard.

Results

For overall tumour detection (n = 32), CT sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) was 83.3%, 100%, 100% and 82.4% respectively. For WB-DWI/MRI these values were 100%, 92.9%, 94.7% and 100%, respectively. For staging (n = 18) malignant lymph nodes and metastases, CT had a sensitivity, specificity/PPV/NPV of 50%/100%/100%/71.4%, and 15.4%/100%/100%/31.3% respectively. For WB-DWI/MRI, all values were 100%, for both malignant lymph nodes and metastases. WB-DWI/MRI was significantly better than CT in detecting tumour infiltration of the mesenteric root, serosal involvement of the small bowel and peritoneal metastases for which WB-DWI/MRI was correct in 100% of these cases, CT 0%.

Conclusions

WB-DWI/MRI is highly accurate for diagnosis, staging and follow-up of patients with suspected gastric cancer.

Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning

IMPORTANCE

Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis.

OBJECTIVE

To assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images.

DESIGN, SETTING, AND PARTICIPANTS

In this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020.

MAIN OUTCOMES AND MEASURES

The area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis.

RESULTS

A total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis.

CONCLUSIONS AND RELEVANCE

The findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.

Providing Reliable Prognosis to Patients with Gastric Cancer in the Era of Neoadjuvant Therapies: Comparison of AJCC Staging Schemata

Purpose

Patients with gastric cancer who receive neoadjuvant therapy are staged before treatment (cStage) and after treatment (ypStage). We aimed to compare the prognostic reliability of cStage and ypStage, alone and in combination.

Materials and Methods

Data for all patients who received neoadjuvant therapy followed by surgery for gastric adenocarcinoma from 2004 to 2015 were extracted from the National Cancer Database. Kaplan-Meier (KM)curves were used to model overall survival based on cStage alone, ypStage alone, cStage stratified by ypStage, and ypStage stratified by cStage. P-values were generated to summarize the differences in KM curves. The discriminatory power of survival prediction was examined using Harrell's C-statistics.

Results

We included 8,977 patients in the analysis. As expected, increasing cStage and ypStage were associated with worse survival. The discriminatory prognostic power provided by cStage was poor (C-statistic 0.548), while that provided by ypStage was moderate (C-statistic 0.634). Within each cStage, the addition of ypStage information significantly altered the prognosis (P<0.0001 within cStages I–IV). However, for each ypStage, the addition of cStage information generally did not alter the prognosis (P=0.2874, 0.027, 0.061, 0.049, and 0.007 within ypStages 0–IV, respectively). The discriminatory prognostic power provided by the combination of cStage and ypStage was similar to that of ypStage alone (C-statistic 0.636 vs. 0.634).

Conclusions

The cStage is unreliable for prognosis, and ypStage is moderately reliable. Combining cStage and ypStage does not improve the discriminatory prognostic power provided by ypStage alone. A ypStage-based prognosis is minimally affected by the initial cStage.

A Nomogram Based on Clinicopathologic Features and Preoperative Hematology Parameters to Predict Occult Peritoneal Metastasis of Gastric Cancer: A Single-Center Retrospective Study

Background

In patients with gastric cancer (GC), peritoneal metastasis is an indication of the end stage and often indicates a poor outcome. The diagnosis of peritoneal metastasis, especially occult peritoneal metastasis (OPM), remains a challenge for surgeons. This study was designed to explore the relationship between OPM and clinicopathological characteristics and preoperative hematological parameters in patients with GC and to develop a nomogram to predict the probability of OPM before surgery.

Methods

A total of 672 patients with GC from our center were included, including 583 OPM-negative and 89 OPM-positive patients. These patients were divided into training and validation groups based on when they received treatment. OPM was diagnosed during surgery in patients without any signs of metastasis through imaging examination. Predictive factors were screened by least absolute shrinkage and selection operator logistic regression of all 18 characteristics. The nomogram of OPM was constructed based on these filtered variables. The discriminative and calibration performance of the model were simultaneously evaluated.

Results

A total of six variables, including tumor size, degree of differentiation, depth of invasion, Glasgow prognosis score, and plasma levels of CA125 and fibrinogen, were selected for integration into the final predictive nomogram. The area under curve (AUC) of the nomogram with six factors was 0.906 (95% confidence interval (CI): 0.872-0.941) and 0.889 (95% CI: 0.795-0.984) in the training and validation groups, respectively. Calibration plots of the nomogram in the two sets revealed a good consistency between predicted and actual probabilities. Decision curve analysis showed that the nomogram had a positive net benefit among all threshold probabilities between 0% and 82%. This nomogram was superior to models incorporating only clinicopathologic or hematologic features.

Conclusion

Both clinicopathological and preoperative hematological parameters are significantly associated with OPM. The nomogram constructed with six factors could be used to calculate the probability of OPM and identify the high-risk population in GC. This may be helpful for early detection of OPM in patients with GC.

Nomogram to Predict Preoperative Occult Peritoneal Metastasis of Gastrointestinal Stromal Tumors (GIST) Based on Imaging and Inflammatory Indexes

Background

Preoperative imaging examination is the primary method for diagnosing metastatic gastrointestinal stromal tumor (GIST), but it is associated with a high rate of missed diagnosis. Therefore, it is important to establish an accurate model for predicting occult peritoneal metastasis (PM) of GIST.

Patients and Methods

GIST patients seen between April 2002 and December 2018 were selected from an institutional database. Using multivariate logistic regression analyses, we created a nomogram to predict occult PM of GIST and validated it with an independent cohort from the same center. The concordance index (C-index), decision curve analysis (DCA) and a clinical impact curve (CIC) were used to evaluate its predictive ability.

Results

A total of 522 eligible GIST patients were enrolled in this study and divided into training (n=350) and validation cohorts (n=172). Factors associated with occult PM were included in the model: tumor size (odds ratio [OR] 1.194 95% confidence interval [CI], 1.034-1.378; p=0.016), primary location (OR 7.365 95% CI, 2.192-24.746; p=0.001), tumor capsule (OR 4.282 95% CI, 1.209-15.166; p=0.024), Alb (OR 0.813 95% CI, 0.693-0.954; p=0.011) and FIB (OR 2.322 95% CI, 1.410-3.823; p=0.001). The C-index was 0.951 (95% CI, 0.917-0.985) in the training cohort and 0.946 (95% CI, 0.900-0.992) in the validation cohort. In the training cohort, the prediction model had a sensitivity of 82.8%, a specificity of 93.8%, a positive predictive value of 54.7%, and a negative predictive value of 98.4%; the validation cohort values were 94.7%, 85.0%, 43.9% and 99.2%, respectively. DCA and CIC results showed that the nomogram had clinical value in predicting occult PM in GIST patients.

Conclusion

Imaging and inflammatory indexes are significantly associated with microscopic metastases of GIST. A nomogram including these factors would have an excellent ability to predict occult PM.

An ascites grading system for predicting the prognosis of gastric cancer with peritoneum dissemination

AIM

Gastric cancer with peritoneum dissemination is intractable with surgical resection. The evaluation of the degree of dissemination using computed tomography (CT) is difficult. We focused on the amount of ascites based on CT findings and established a scaling system to predict these patients' prognoses.

METHODS

We extracted individual data from a population-based cohort. Patients diagnosed with histologically proven gastric adenocarcinoma with peritoneum dissemination were enrolled. Two raters evaluated the CT images and determined the grade of ascites in each patient: grade 0 indicated no ascites in all slices; grade 1 indicated ascites detected only in the upper or lower abdominal cavity; grade 2 indicated ascites detected in both the upper and lower abdominal cavities; and grade 3 indicated ascites extending continuously from the pelvic cavity to the upper abdominal cavity. We evaluated the relationship between the ascites grade and survival time. After adjusting for other clinical factors, we calculated hazard ratios of each ascites grade.

RESULTS

A total of 718 patients were enrolled. The number of patients with grades 0, 1, 2, and 3 were 303, 223, 94, and 98, respectively. The median overall survival times were 16.0, 8.7, 5.4, and 3.0 months for ascites on CT grades 0, 1, 2, and 3, respectively (P < .001). The adjusted hazard ratios for the survival time were 1.74 (1.33-2.26, P < .001), 3.20 (2.25-4.57, P < .001), and 4.76 (3.16-7.17, P < .001) for grades 1, 2, and 3, respectively.

CONCLUSION

We established a new grading system of pretreatment ascites to better predict the prognosis of gastric cancer.

An Intraoperative Model for Predicting Survival and Deciding Therapeutic Schedules: A Comprehensive Analysis of Peritoneal Metastasis in Patients With Advanced Gastric Cancer

Background and Objective: No specialized prognostic model for patients with gastric cancer with peritoneal metastasis (GCPM) exists for intraoperative clinical decision making. This study aims to establish a new prognostic model to provide individual treatment decisions for patients with GCPM.

Method: This retrospective analysis included 324 patients with GCPM diagnosed pathologically by laparoscopy from January 2007 to January 2018 who were randomly assigned to different sets (227 in the training set and 97 in the internal validation set). A nomogram was established from preoperative and intraoperative variables determined by a Cox model. The predictive ability and clinical applicability of the PM nomogram (PMN) were compared with the 15th Japanese Classification of Gastric Carcinoma (JCGC) Staging Guidelines for PM (P1abc). Additional external validation was performed using a dataset (n = 39) from the First Affiliated Hospital of University of Science and Technology of China.

Results: The median survival time was 8 (range, 1–90) months. In the training set, each PMN substage had significantly different survival curves (P < 0.001), and the PMN was superior to the P1abc based on the results of time-dependent receiver operating characteristic curve, C-index, Akaike information criterion and likelihood ratio chi-square analyses. In the internal and external validation sets, the PMN was also better than the P1abc in terms of its predictive ability. Of the PMN1 patients, those undergoing palliative resection had better overall survival (OS) than those undergoing exploratory surgery (P < 0.05). Among the patients undergoing exploratory surgery, those who received chemotherapy exhibited better OS than those who did not (P < 0.05). Among the patients who received palliative resection, only PMN1 patients exhibited better OS following chemotherapy (P < 0.05).

Conclusion: We developed and validated a simple, specific PM model for patients with GCPM that can predict prognosis well and guide treatment decisions.

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status.

Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram.

Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703–0.799), 0.802 (95%CI, 0.691–0.912), and 0.745 (95%CI, 0.683–0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748–0.836), 0.870 (95%CI, 0.795–0.946), and 0.815 (95%CI, 0.763–0.867), respectively.

Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.