Value of multiphase contrast-enhanced CT with three-dimensional reconstruction in detecting depth of infiltration, lymph node metastasis, and extramural vascular invasion of gastric cancer

Comparison of [99mTc]Tc-FAPI SPECT/CT and [18F]FDG PET/CT as predictive biomarkers for immunotherapy response in gastrointestinal cancer

To explore the diagnostic performance of [99mTc]Tc-FAPI SPECT/CT for gastrointestinal cancer, compared to [18F]FDG PET/CT. In this analysis of a prospective trial, consecutively recruited patients from a single center with pathologically confirmed gastrointestinal cancer were prospectively enrolled from September 2022 to June 2024 and underwent paired v and [18F]FDG PET/CT examinations at intervals of more than 1 day and within 7 days of each other.The activity of tracer accumulation in lesions was assessed by maximum standardized uptake value(SUVmax) and TBR (lesions SUVmax/ascending aorta SUVmean). Histopathologic and clinical follow-up results were used as reference standards for final diagnoses. Seventy-eight patients (46 men; median age, 58.8 ± 14.5 years) were evaluated. Compared with the TBR for [18F]FDG uptake, TBR for [99mTc]Tc-FAPI uptake was higher in primary tumor(4.6 ± 2.0 vs. 3.4 ± 1.7; P = 0.001) ,peritoneal spread (1.3 [1.1,7.3] vs. 1.1[1.1,1.1]; P = 0.001 ) and liver metastases( 2.5[1.1,8.5] vs. 1.1[1.1,3.4]; P = 0.031). For diagnostic accuracy in a total of 253 lesions in 78 patients, compared with [18F]FDG PET/CT, [99mTc]Tc-FAPI SPECT/CT demonstrated a higher sensitivity (100% [15 of 15 lesions] vs. 20% [3 of 15]; P < 0.001), accuracy (100% [48 of 48 lesions] vs. 75% [36 of 48];P < 0.001), and negative predictive value (100% [33 of 33 lesions] vs. 69% [36 of 48 lesions]; P = 0 0.001) in detecting peritoneal spread, and a higher sensitivity (85% [17 of 20 lesions] vs. 50% [10 of 20]; P = 0.041) in detecting liver metastases. Patients with metastatic gastrointestinal carcinomas negative on the [99mTc]Tc-FAPI scan showed improved clinical prognosis after immunotherapy (P<0.006). TBR-FDG/TBR-FAPI was the main predictor of better prognosis post-immunotherapy ([stable disease, SD]+[partial response, PR]), with an optimal cut-off of 3.82. [99mTc]Tc-FAPI SPECT/CT can better evaluate peritoneal spread and liver metastases in gastrointestinal cancer. Furthermore, TBR-FDG/TBR-FAPI is a valuable imaging parameter for monitoring immunotherapy responses.

Development and Validation of an AI-Based Multimodal Model for Pathological Staging of Gastric Cancer Using CT and Endoscopic Images

RATIONALE AND OBJECTIVES

Accurate preoperative pathological staging of gastric cancer is crucial for optimal treatment selection and improved patient outcomes. Traditional imaging methods such as CT and endoscopy have limitations in staging accuracy.

METHODS

This retrospective study included 691 gastric cancer patients treated from March 2017 to March 2024. Enhanced venous-phase CT and endoscopic images, along with postoperative pathological results, were collected. We developed three modeling approaches: (1) nine deep learning models applied to CT images (DeepCT), (2) 11 machine learning algorithms using handcrafted radiomic features from CT images (HandcraftedCT), and (3) ResNet-50-extracted deep features from endoscopic images followed by 11 machine learning algorithms (DeepEndo). The two top-performing models from each approach were combined into the Integrated Multi-Modal Model using a stacking ensemble method. Performance was assessed using ROC-AUC, sensitivity, and specificity.

RESULTS

The Integrated Multi-Modal Model achieved an ROC-AUC of 0.933 (95% CI, 0.887-0.979) on the test set, outperforming individual models. Sensitivity and specificity were 0.869 and 0.840, respectively. Various evaluation metrics demonstrated that the final fusion model effectively integrated the strengths of each sub-model, resulting in a balanced and robust performance with reduced false-positive and false-negative rates.

CONCLUSION

The Integrated Multi-Modal Model effectively integrates radiomic and deep learning features from CT and endoscopic images, demonstrating superior performance in preoperative pathological staging of gastric cancer. This multimodal approach enhances predictive accuracy and provides a reliable tool for clinicians to develop individualized treatment plans, thereby improving patient outcomes.

DATA AVAILABILITY

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons. All code used in this study is based on third-party libraries and all custom code developed for this study is available upon reasonable request from the corresponding author.

Correction of stomach cancer CT attenuation values for variations due to differences in CT imaging conditions through repeated CT scans

PURPOSE

To develop methods for correcting variations in CT attenuation values of advanced gastric cancer (AGC) due to differences in CT imaging conditions using repeated pre-treatment CT scans.

METHODS

A total 211 patients (146 men) with AGC who underwent pre-treatment CT twice were included in this retrospective study. The Pearson correlation between the difference in tumor attenuation values measured on both CT scans and the difference in attenuation values of other organs was analyzed. A formula to correct tumor CT attenuation values was developed using univariate linear regression analysis.

RESULTS

The Pearson correlation coefficient was the highest between the difference in tumor attenuation values and that of the main portal vein (MPV) attenuation values (0.86, P <.01). The formula to correct tumor attenuation values was as follows: calculated tumor attenuation value on CT scan 2 = tumor attenuation value on CT scan 1 - (-3.5 + 0.4 x (MPV attenuation value on CT scan 1 - MPV attenuation value on CT scan 2)). The mean difference between calculated and actual tumor attenuation values was 1.6 HU (SD, 8.7; range -22.5-24.72), with a Pearson correlation coefficient of 0.95 (P <.01).

CONCLUSION

Utilizing the attenuation value of the MPV allows for correction of variations in tumor attenuation values caused by different CT imaging conditions, enabling the prediction of reproducible tumor attenuation in patients with AGC. Future studies are needed to validate these findings and address the study's limitations, including its retrospective design and the absence of unenhanced CT data.

ADVANCES IN KNOWLEDGE

The attenuation value of the MPV can be used to predict reproducible tumor attenuation values in gastric cancer.

CT-based scores for extramural vascular invasion and occult peritoneal metastasis correlate with gastric cancer survival

OBJECTIVE

To assess the feasibility of scoring extragastric vascular invasion and occult peritoneal metastasis using preoperative computed tomography (CT) images of gastric cancer (GC) and to explore the correlation between these scores and patient prognosis.

METHODS

587 GC patients with CT scans from two centers, all confirmed by pathology, were retrospectively evaluated. Scores for CT-detected blood vessel invasion (ctBVI), lymphatic invasion (ctLVI), and occult peritoneal metastasis (ctOPM) were assigned based on preoperative CT images. The patients' follow-up provided data on overall and disease-free survival. Cox proportional hazard models were used to analyze prognostic factors.

RESULTS

The inter-group and intra-group consistency of ctBVL, ctLVI, and ctOPM scores were all > 0.70. Log-rank analysis demonstrated a statistically significant difference in survival curves (p < 0.001). CtBVL, ctLVI, and ctOPM scores were related to overall survival (OS) and disease-free survival (DFS). Univariate and multivariate Cox regression analyses identified ctBVL, ctLVI, ctOPM scores as independent risk factors for GC prognosis. In multivariate analysis, the three sign scores were related to DFS (p < 0.05), with ctBVL (hazard ratio (HR) = 1.980, 95% CI: 1.336-2.933), ctLVI (HR = 1.502, 95% CI: 1.336-2.933), and ctOPM (HR = 1.182, 95% CI: 0.886-1.578). The three scores were also correlated with OS (p < 0.05), ctBVL (HR = 2.003, 95% CI: 1.278-3.139), ctLVI (HR = 1.523, 95% CI:1.055-2.200) and ctOPM (HR = 1.289, 95% CI: 1.013-1.770).

CONCLUSION

CtBVL, ctLVI, and ctOPM scores are valuable prognostic indicators in gastric cancer, influencing both OS and DFS.

KEY POINTS

Question To study whether the ctBVL, ctLVI, and ctOPM scores assessed by preoperative enhanced CT imaging can predict the survival outcomes of patients. Findings CtBVL, ctLVI, and ctOPM scores, assessed via preoperative enhanced CT imaging, are associated with worse survival outcomes when elevated. Clinical relevance CtBVL, ctLVI, and ctOPM scores may help guide personalized follow-up plans. Patients with higher scores might require closer monitoring and more aggressive treatment.

Identification of cancer-associated fibrolast subtypes and distinctive role of MFAP5 in CT-detected extramural venous invasion in gastric cancer

Extramural venous invasion (EMVI) detected by computed tomography has been identified as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). Cancer-associated fibroblasts (CAFs) are critical for remodeling the tumor microenvironment in GCs. Here, we report that MFAP5+ CAFs promote the formation of EMVI imaging in GC. We detected gene expression in pathological samples from 13 advanced GC patients with EMVI. Radiogenomics results showed the degree of CAFs infiltration was directly proportional to the EMVI score and EMT pathway in GC patients. Single-cell sequencing data analysis results showed that MFAP5+CAFs subtypes in GC were negatively correlated with patient prognosis and were enriched in tumor lactylation modification and EMT pathways. Immunohistochemistry results showed that the expression of MFAP5, L-lactyl and EMT markers in GC tissues was proportional to the EMVI score. CAF from gastric cancer tissue was extracted using collagenase method and co-cultured with GC cell line in vitro. After lentivirus knockdown of MFAP5 in CAFs, the levels of L-lactoyl and histone lactylation modifications were significantly reduced, and the sphere-forming and vascularization abilities of CAFs were significantly inhibited. Cell function experiments showed that MFAP5+ CAFs can affect the EMT, metastasis and invasion capabilities of GC cells. In vivo experimental results of the nude mouse in situ EMVI model suggest that MFAP5+ CAF may promote the formation of EMVI imaging features in GC by regulating lactylation modification. This innovative work may provide important new references for the diagnosis and treatment of GC.

A quantitative model using multi-parameters in dual-energy CT to preoperatively predict serosal invasion in locally advanced gastric cancer

OBJECTIVES

To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).

MATERIALS AND METHODS

A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.

RESULTS

A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.

CONCLUSION

The proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.

CRITICAL RELEVANCE STATEMENT

This quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.

KEY POINTS

Serosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival.

Novel computed tomography-based nomograms for the pretherapeutic prediction of response to neoadjuvant chemotherapy with S-1 and oxaliplatin with or without the addition of docetaxel in patients with advanced gastric cancer

BACKGROUND

Selecting the appropriate preoperative neoadjuvant chemotherapy (NACT) regimen for patients with advanced gastric cancer (GC) is critical to effective treatment. The aim of this study was to develop nomograms based on pretherapeutic computed tomography (CT) features to predict response to NACT with S-1 and oxaliplatin (SOX) or that with docetaxel and SOX (DOS) in patients with advanced GC.

METHODS

This study enrolled 311 consecutive patients with confirmed advanced GC undergoing contrast-enhanced CT before and after the three cycles of NACT with DOS (n=152) or SOX (n=159), who were randomized into a training cohort (TC) (NACT with DOS: n=111; NACT with SOX: n=120) and validation cohort (VC) (NACT with DOS: n=41; NACT with SOX: n=39). The objective response rate (ORR) was used to evaluate the response to NACT. In the TC, ORR was compared between the DOS and SOX regimens, and independent predictors including CT features and tumor differentiation were determined by univariate and binary logistic regression analyses. Individual nomograms were constructed for the SOX and DOS regimens in the TC, and the predictive accuracy was validated in the VC.

RESULTS

After NACT, the percentage of ORR was higher in patients receiving DOS than in those receiving SOX in TC (P value <0.05). The independent predictors after DOS and SOX were pretherapeutic cT stage [odds ratio (OR) =7.364; OR =8.848], cN stage (OR =1.027; OR =1.345), degree of differentiation (OR =7.127; OR =7.835), and gross tumor volume (OR =8.960; OR =8.161) (all P values <0.05). The concordance indexes of the individual nomograms developed using these predictors were 0.940 and 0.932 after DOS or SOX in the TC, respectively, which was validated by calibration plots with a slope close to 45° in the TC and VC.

CONCLUSIONS

Despite there being a superior response to DOS compared with SOX, nomograms for predicting response to both NACT regimens were similar, with each demonstrating good predictive performance.

Radiomics analysis in predicting vascular invasion in gastric cancer based on enhanced CT: a preliminary study

BACKGROUND

Vascular invasion (VI) is closely related to the metastasis, recurrence, prognosis, and treatment of gastric cancer. Currently, predicting VI preoperatively using traditional clinical examinations alone remains challenging. This study aims to explore the value of radiomics analysis based on preoperative enhanced CT images in predicting VI in gastric cancer.

METHODS

We retrospectively analyzed 194 patients with gastric adenocarcinoma who underwent enhanced CT examination. Based on pathology analysis, patients were divided into the VI group (n = 43) and the non-VI group (n = 151). Radiomics features were extracted from arterial phase (AP) and portal venous phase (PP) CT images. The radiomics score (Rad-score) was then calculated. Prediction models based on image features, clinical factors, and a combination of both were constructed. The diagnostic efficiency and clinical usefulness of the models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

RESULTS

The combined prediction model included the Rad-score of AP, the Rad-score of PP, Ki-67, and Lauren classification. In the training group, the area under the curve (AUC) of the combined prediction model was 0.83 (95% CI 0.76-0.89), with a sensitivity of 64.52% and a specificity of 92.45%. In the validation group, the AUC was 0.80 (95% CI 0.67-0.89), with a sensitivity of 66.67% and a specificity of 88.89%. DCA indicated that the combined prediction model might have a greater net clinical benefit than the clinical model alone.

CONCLUSION

The integrated models, incorporating enhanced CT radiomics features, Ki-67, and clinical factors, demonstrate significant predictive capability for VI. Moreover, the radiomics model has the potential to optimize personalized clinical treatment selection and patient prognosis assessment.

Construction of a preoperative nomogram model for predicting perineural invasion in advanced gastric cancer

Objective

This study aimed to develop and validate a clinical and imaging-based nomogram for preoperatively predicting perineural invasion (PNI) in advanced gastric cancer.

Methods

A retrospective cohort of 351 patients with advanced gastric cancer who underwent surgical resection was included. Multivariable logistic regression analysis was conducted to identify independent risk factors for PNI and to construct the nomogram. The performance of the nomogram was assessed using calibration curves, the concordance index (C-index), the area under the curve (AUC), and decision curve analysis (DCA). The disparity in disease-free survival (DFS) between the nomogram-predicted PNI-positive group and the nomogram-predicted PNI-negative group was evaluated using the Log-Rank test and Kaplan-Meier analysis.

Results

Extramural vascular invasion (EMVI), Borrmann classification, tumor thickness, and the systemic inflammation response index (SIRI) emerged as independent risk factors for PNI. The nomogram model demonstrated a commendable AUC value of 0.838. Calibration curves exhibited excellent concordance, with a C-index of 0.814. DCA indicated that the model provided good clinical net benefit. The DFS of the nomogram-predicted PNI-positive group was significantly lower than that of the nomogram-predicted PNI-negative group (p < 0.001).

Conclusion

This study successfully developed a preoperative nomogram model that not only effectively predicted PNI in gastric cancer but also facilitated postoperative risk stratification.

Development and validation of a preoperative CT-based risk scoring system for predicting recurrence-free survival in patients undergoing curative surgery for gastric cancer

PURPOSE

The objective of this study was to establish and validate a preoperative risk scoring system that incorporated both clinical and computed tomography(CT) variables to predict recurrence-free survival (RFS) in gastric cancer(GC) patients who underwent curative resection.

METHOD

We retrospectively included consecutive patients with surgically confirmed GC who underwent preoperative CT scans between October 2017 and January 2022. Multivariate Cox regression analysis was employed in the derivation set to identify clinical and CT variables associated with RFS and to construct a risk score. This risk score was subsequently validated in an independent test set.

RESULTS

A total of 346 patients were included in the study, with 213 in the derivation set and 133 in the test set. Five variables, namely ctEMVI, ctBorrmann, visceral obesity, sarcopenia, and NLR, were independently associated with RFS. In the test set, the preoperative risk score exhibited a c-index of 0.741, which outperformed the predictive accuracy of pathological tumor staging (c-index of 0.673, p = 0.021) at various time points. The preoperative risk score effectively stratified patients into low and high-risk groups.

CONCLUSION

The developed preoperative risk scoring system demonstrated the ability to predict RFS following curative resection in GC patients.

Computed tomography-based nomogram of Siewert type II/III adenocarcinoma of esophagogastric junction to predict response to docetaxel, oxaliplatin and S-1

BACKGROUND

Neoadjuvant chemotherapy (NAC) has become the standard care for advanced adenocarcinoma of esophagogastric junction (AEG), although a part of the patients cannot benefit from NAC. There are no models based on baseline computed tomography (CT) to predict response of Siewert type II or III AEG to NAC with docetaxel, oxaliplatin and S-1 (DOS).

AIM

To develop a CT-based nomogram to predict response of Siewert type II/III AEG to NAC with DOS.

METHODS

One hundred and twenty-eight consecutive patients with confirmed Siewert type II/III AEG underwent CT before and after three cycles of NAC with DOS, and were randomly and consecutively assigned to the training cohort (TC) (n = 94) and the validation cohort (VC) (n = 34). Therapeutic effect was assessed by disease-control rate and progressive disease according to the Response Evaluation Criteria in Solid Tumors (version 1.1) criteria. Possible prognostic factors associated with responses after DOS treatment including Siewert classification, gross tumor volume (GTV), and cT and cN stages were evaluated using pretherapeutic CT data in addition to sex and age. Univariate and multivariate analyses of CT and clinical features in the TC were performed to determine independent factors associated with response to DOS. A nomogram was established based on independent factors to predict the response. The predictive performance of the nomogram was evaluated by Concordance index (C-index), calibration and receiver operating characteristics curve in the TC and VC.

RESULTS

Univariate analysis showed that Siewert type (52/55 vs 29/39, P = 0.005), pretherapeutic cT stage (57/62 vs 24/32, P = 0.028), GTV (47.3 ± 27.4 vs 73.2 ± 54.3, P = 0.040) were significantly associated with response to DOS in the TC. Multivariate analysis of the TC also showed that the pretherapeutic cT stage, GTV and Siewert type were independent predictive factors related to response to DOS (odds ratio = 4.631, 1.027 and 7.639, respectively; all P < 0.05). The nomogram developed with these independent factors showed an excellent performance to predict response to DOS in the TC and VC (C-index: 0.838 and 0.824), with area under the receiver operating characteristic curve of 0.838 and 0.824, respectively. The calibration curves showed that the practical and predicted response to DOS effectively coincided.

CONCLUSION

A novel nomogram developed with pretherapeutic cT stage, GTV and Siewert type predicted the response of Siewert type II/III AEG to NAC with DOS.

Study on the diagnostic value of MDCT extramural vascular invasion in preoperative N staging of gastric cancer patients

BACKGROUND

To explore the diagnostic value of multidetector computed tomography (MDCT) extramural vascular invasion (EMVI) in preoperative N Staging of gastric cancer patients.

METHODS

According to the MR-defined EMVI scoring standard of rectal cancer, we developed a 5-point scale scoring system to evaluate the status of CT-detected extramural vascular invasion(ctEMVI), 0-2 points were ctEMVI-negative status, and 3-4 points were positive status for ctEMVI. Patients were divided into ctEMVI positive group and ctEMVI negative group. The correlation between ctEMVI and clinical features was analyzed. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of ctEMVI for pathological metastatic lymph nodes and N staging, The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of pathological N staging using ctEMVI and short-axis diameter were generated and compared.

RESULTS

The occurrence rate of lymphovascular invasion (LVI) and proportion of tumors with a greatest diameter > 6 cm in the ctEMVI positive group was higher than that in the ctEMVI negative group (P < 0.05). Spearman correlation analysis showed a positive correlation between ctEMVI and LVI, N stage, and tumor size (P < 0.05). For ctEMVI scores ≥ 3,The AUC of ctEMVI for diagnosing lymph node metastasis, N stage ≥ N2, and N3 stage were 0.857, 0.802, and 0.758, respectively. The sensitivity, NPV and accuracy of ctEMVI for diagnosing N stage ≥ N2 were superior to those of short-axis diameter (P < 0.05), while sensitivity, specificity, PPV, NPV, and accuracy of ctEMVI for diagnosing N3 stage were superior to those of short-axis diameter (P < 0.05).

CONCLUSION

ctEMVI has important value in diagnosing metastatic lymph nodes and advanced N staging. As an important imaging marker, ctEMVI can be included in the preoperative imaging evaluation of patients, providing important assistance for clinical guidance and treatment.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

OBJECTIVE

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.

METHODS

A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.

RESULTS

In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).

CONCLUSION

The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.

CRITICAL RELEVANCE STATEMENT

MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.

KEY POINTS

• Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.

Efficacy of multi-slice spiral computed tomography in evaluating gastric cancer recurrence after endoscopic submucosal dissection

BACKGROUND

Recurrence is the major challenge facing endoscopic submucosal dissection (ESD)-based treatment therapies for early gastric cancer (EGC). Urgent development of simple and easy surveillance approaches will enhance clinical treatment of the disease.

AIM

To explore the role of computed tomography (CT) recurrence in evaluating EGC after ESD treatment.

METHODS

We retrospectively recruited patients from our endoscopy department, between January 2002 and December 2015, and analyzed their basic characteristics, including symptoms, CT results, and results of endoscopy with biopsy, among others.

RESULTS

Among a total of 2150 patients EGC patients surveyed, 1362 met our inclusion and exclusion criteria and were therefore enrolled in our study. The cohort's sensitivity of CT for recurrent GC and specificity were 44.22% and 43.86%, respectively, with negative and positive predictive values of 40.15% (275/685) and 48.01% (325/677), respectively. The area under the curve of arterial and venous CT values for recurrent EGC were 0.545, and 0.604, respectively. Receiver operating characteristic curve revealed no statistically significant differences between arterial and venous CT values for recurrent EGC.

CONCLUSION

Enhanced CT has superior diagnostic efficacy, but less accuracy, compared to gold standard techniques in patients with recurrent EGC.

Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning

Vessel invasion (VI) is an important factor affecting the prognosis of gastric cancer (GC), and the accurate determination of preoperative VI for locally advanced GC is of great clinical significance. Traditional methods for the evaluation of VI require postoperative pathological examination. Noninvasive preoperative evaluation of VI is therefore crucial to determine the best treatment strategy. To determine the value of preoperative prediction of gastric VI based on portal venous phase computed tomography (CT) radiomic features and machine-learning models, a retrospective analysis of 296 patients with locally advanced GC confirmed through pathological examination was performed. They were divided into two groups, VI+ (n=213) and VI- (n=83), based on pathological results. Using pyradiomics to extract two-dimensional radiomic features of the portal venous stage of locally advanced GC, data were divided into training (n=207) and validation sets (n=89), with a ratio of 7:3, and three feature selection methods were cascaded and merged. Finally, least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to obtain the optimal feature subset. Four current representative machine-learning algorithms were used to construct the prediction model, the receiver operating characteristic curve was constructed to evaluate the predictive performance of the model, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. The differentiation degree, and the Lauren's and CA199 classifications were independent risk factors for locally advanced GC VI. Pyradiomics extracted 864 quantitative features of portal vein images of locally advanced GC. After filtering out low variance features using R, 236 features remained. Next, 18 features were screened using the LASSO algorithm. Extreme gradient boosting (XGBoost), logistic regression, Gaussian naive Bayes, and support vector machine models were constructed based on the 18 best features screened out of the portal venous CT images of advanced GC and three independent risk factors of GC VI in clinical features predicted the training set AUC values of 0.914, 0.897, 0.880, and 0.814, respectively. The predicted validation set AUC values were 0.870, 0.877, 0.859, and 0.773, respectively. The DeLong test results indicated no statistically significant difference in AUC values between the XGBoost and logistic regression models in the training and validation sets. The four machine-learning models showed high predictive performance. The logistic regression model had the highest AUC value in the validation set (0.877), and the accuracy and F1 score were 77 and 87.6%, respectively. CT radiomic features and machine-learning models based on the portal venous phase can be used as a noninvasive imaging method for the preoperative prediction of VI in locally advanced GC. The logistic regression model exhibited the highest diagnostic performance.

Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer

BACKGROUND

Precise preoperative prediction of lymph node metastasis (LNM) is crucial for optimal diagnosis and treatment in patients with gastric cancer (GC), in which existing imaging methods have certain limitations. We hypothesized that PET primary lesion-based radiomics signature could provide incremental value to conventional metabolic parameters and traditional risk indicators in predicting LNM in patients with GC.

METHODS

This retrospective study was performed in 127 patients with GC who underwent preoperative PET/CT. Basic clinical data and PET conventional metabolic parameters were collected. Radiomics signature was constructed by the least absolute shrinkage and selection operator algorithm (LASSO) logistic regression. Based on the postoperative histological results, the patients were divided into LNM group and non-lymph node metastasis (NLNM) group. Receiver-operating characteristic (ROC) was used to evaluate the discriminatory ability of Radiomics score (Rad-score) for predicting LNM and determine whether adding Rad-score to PET conventional metabolic parameters and traditional risk factors could improve the predictive value in LNM. The Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to further confirm the incremental value of Rad-score for predicting LNM in GC.

RESULTS

The LNM group had higher Rad-score than NLNM group [(0.35 (-0.13-0.85) vs. -0.61 (-1.92-0.18), P < 0 .001)]. After adjusted for gender, age, BMI, and FBG, multivariable logistic regression analysis illustrated that Rad-score (OR: 6.38, 95% CI: 2.73-14.91, P < 0.0001) was independent risk factors for LNM in GC. Adding PET conventional parameters to traditional risk factors increased the predictive value of LNM in GC (AUC 0.751 vs 0.651, P = 0.02). Additional inclusion of Rad-score to conventional metabolic parameters and traditional risk indicators significantly improved the AUC (0.882 vs 0.751; P = 0.006). Bootstrap resampling (times = 500) was used for internal verification, 95% confidence interval (CI) was 0.802-0.948, with the sensitivity equaled to 89.5%, and positive predictive value (PPV) was 93.5%. When Rad-score was added to conventional metabolic parameters and traditional risk indicators, net reclassification improvement (NRI) was 0.293 (P = 0.0040) and integrated discrimination improvement (IDI) was 0.293 (P = 0.0045).

CONCLUSION

In GC patients, PET Radiomics signature of the primary lesion-based was significantly associated with LNM and could improve the prediction of LNM above PET conventional metabolic parameters and traditional risk factors, which could provide incremental value for individual diagnosis and treatment of GC.

Case Report: A case of uterine leiomyosarcoma metastasized to the vena cava, excised with the aid of preoperative CT three-dimensional imaging

Leiomyosarcoma of the uterus (ULMS) is a rare malignant tumor originating from embryonic mesenchymal cells. ULMS tends to metastasize to the lungs, lymph nodes, liver, and bone. Computed tomography three-dimensional (CT 3D) imaging is an advanced diagnostic technique that can track the vessels and their relationships with tumors and reveal the invasion of vessels, including small vessels, around tumors in any slice. Here, we describe a case in which ULMS extended to the retrohepatic inferior vena cava. To date, no report has described resection of metastatic ULMS of the vena cava through supplemental CT 3D imaging. Our patient presented with right lumbar abdominal pain as the main symptom. After using CT 3D reconstruction to accurately assess the relationship between the tumor and the surrounding organs and blood vessels before the operation, the operation was successfully completed through multidisciplinary surgical collaboration.

Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma

Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologists, considering only loco-regional LNs with a long axis (LA) ≥ 5 mm. For each nodal group, the short axis (SA), volume and SA/LA ratio of the largest LN, the sum of the SAs of all LNs, and the mean of the SA/LA ratios were plotted in ROC curves, taking the presence/absence of metastases at histopathology for reference. On a per-patient basis, the sums of the SAs of all LNs, and the sums of the SAs, volumes, and SA/LA ratios of the largest LNs in all nodal groups were also plotted, taking the presence/absence of metastatic LNs in each patient for reference. Results. Four hundred and forty-three nodal groups were harvested during surgery from 107 patients with GC, and 173 (39.1%) were metastatic at histopathology. By nodal group, the sum of the SAs showed the best Area Under the Curve (AUC), with a sensitivity/specificity of 62.4/72.6% using Youden’s index with a >8 mm cutoff. In the per-patient analysis, the sum of the SAs of all LNs in the loco-regional nodal groups showed the best AUC with a sensitivity/specificity of 65.6%/83.7%, using Youden’s index with a >39 mm cutoff. Conclusion. In patients with GC, the sum of the SAs of all the LNs at staging CT is the best predictor among dimensional LNs criteria of both metastatic invasion of the nodal group and the presence of metastatic LNs.

Significance of Lymph Node Metastasis in the Treatment of Gastric Cancer and Current Challenges in Determining the Extent of Metastasis

The stomach exhibits abundant lymphatic flow, and metastasis to lymph nodes is common. In the case of gastric cancer, there is a regularity to the spread of lymph node metastasis, and it does not easily metastasize outside the regional nodes. Furthermore, when its extent is limited, nodal metastasis of gastric cancer can be cured by appropriate lymph node dissection. Therefore, identifying and determining the extent of lymph node metastasis is important for ensuring accurate diagnosis and appropriate surgical treatment in patients with gastric cancer. However, precise detection of lymph node metastasis remains difficult. Most nodal metastases in gastric cancer are microscopic metastases, which often occur in small-sized lymph nodes, and are thus difficult to diagnose both preoperatively and intraoperatively. Preoperative nodal diagnoses are mainly made using computed tomography, although the specificity of this method is low because it is mainly based on the size of the lymph node. Furthermore, peripheral nodal metastases cannot be palpated intraoperatively, nodal harvesting of resected specimens remains difficult, and the number of lymph nodes detected vary greatly depending on the skill of the technician. Based on these findings, gastrectomy with prophylactic lymph node dissection is considered the standard surgical procedure for gastric cancer. In contrast, several groups have examined the value of sentinel node biopsy for accurately evaluating nodal metastasis in patients with early gastric cancer, reporting high sensitivity and accuracy. Sentinel node biopsy is also important for individualizing and optimizing the extent of uniform prophylactic lymph node dissection and determining whether patients are indicated for function-preserving curative gastrectomy, which is superior in preventing post-gastrectomy symptoms and maintaining dietary habits. Notably, advancements in surgical treatment for early gastric cancer are expected to result in individualized surgical strategies with sentinel node biopsy. Chemotherapy for advanced gastric cancer has also progressed, and conversion gastrectomy can now be performed after downstaging, even in cases previously regarded as inoperable. In this review, we discuss the importance of determining lymph node metastasis in the treatment of gastric cancer, the associated difficulties, and the need to investigate strategies that can improve the diagnosis of lymph node metastasis.