Keyword: Gastric Cancer
2 results found.
Congress Abstract
Oncology, Nuclear Medicine and Transplantology, 2(3, Suppl. 1), 2026, onmt_A20, https://doi.org/10.63946/onmt/19304
ABSTRACT:
Introduction: Given the difficulties in assessing response of the primary tumor and metastatic lesions in gastric cancer patients using standard RECIST 1.1 criteria, there is a need to search for new biomarkers to evaluate treatment efficacy, especially at early stages after therapy initiation. Perfusion computed tomography is a functional imaging technique that provides qualitative and quantitative information about tumor microcirculation and can serve as a tool for predicting or assessing treatment response, helping to optimize and individualize subsequent patient management.
Objective: To evaluate the role of perfusion CT (PCT) in monitoring response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer.
Materials and Methods: The results of PCT in 28 patients aged 36 to 76 years with histologically confirmed gastric cancer who received combined treatment at the A.F. Tsyb Medical Radiological Research Center between June 2023 and June 2026 were analyzed. Baseline CT, supplemented by perfusion imaging, was performed before treatment initiation to assess tumor extent and obtain baseline perfusion parameters. Follow-up PCT was performed before surgery to evaluate treatment efficacy and changes in perfusion parameters.
Patients were divided into 2 groups: 12 of 28 patients with regression grade 1a/b according to the scale established by K. Becker (2003) were considered "responders" to neoadjuvant chemotherapy, and 16 of 28 patients with regression grade 2/3 were considered "non-responders." Quantitative PCT analysis was based on interpretation of perfusion parameter values automatically calculated from the region of interest (ROI) placed within the tumor. The following PCT parameters were analyzed: blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability surface area (PS).
Results: The obtained perfusion data were subjected to both qualitative and quantitative analysis. Qualitative analysis included interpretation of parametric perfusion maps automatically generated by the software for each perfusion parameter. For each group, the significance of changes in each parameter was assessed using the paired Wilcoxon test. In patients who responded to treatment, a statistically significant decrease in BF, BV, and PS perfusion parameters was observed (p < 0.05). In patients who did not respond to treatment, none of the parameters changed significantly (p > 0.1 for all). Differences in BF and BV dynamics between groups were highly significant (p < 0.01), with significantly greater changes in responders. For PS, the difference was also significant (p = 0.04), although less pronounced. When assessing the prognostic value of baseline PCT parameters, none of the parameters reached statistical significance (p > 0.05); only PS showed a weak trend toward lower values in the responder group.
Conclusion: CT perfusion parameters reflect tissue vascularization and can serve as objective quantitative biomarkers of tumor response to neoadjuvant treatment. Baseline low PS values are associated with a likelihood of clinical response to preoperative chemotherapy in gastric cancer. Further studies with larger sample sizes are needed to clarify the prognostic role of PCT.
Objective: To evaluate the role of perfusion CT (PCT) in monitoring response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer.
Materials and Methods: The results of PCT in 28 patients aged 36 to 76 years with histologically confirmed gastric cancer who received combined treatment at the A.F. Tsyb Medical Radiological Research Center between June 2023 and June 2026 were analyzed. Baseline CT, supplemented by perfusion imaging, was performed before treatment initiation to assess tumor extent and obtain baseline perfusion parameters. Follow-up PCT was performed before surgery to evaluate treatment efficacy and changes in perfusion parameters.
Patients were divided into 2 groups: 12 of 28 patients with regression grade 1a/b according to the scale established by K. Becker (2003) were considered "responders" to neoadjuvant chemotherapy, and 16 of 28 patients with regression grade 2/3 were considered "non-responders." Quantitative PCT analysis was based on interpretation of perfusion parameter values automatically calculated from the region of interest (ROI) placed within the tumor. The following PCT parameters were analyzed: blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability surface area (PS).
Results: The obtained perfusion data were subjected to both qualitative and quantitative analysis. Qualitative analysis included interpretation of parametric perfusion maps automatically generated by the software for each perfusion parameter. For each group, the significance of changes in each parameter was assessed using the paired Wilcoxon test. In patients who responded to treatment, a statistically significant decrease in BF, BV, and PS perfusion parameters was observed (p < 0.05). In patients who did not respond to treatment, none of the parameters changed significantly (p > 0.1 for all). Differences in BF and BV dynamics between groups were highly significant (p < 0.01), with significantly greater changes in responders. For PS, the difference was also significant (p = 0.04), although less pronounced. When assessing the prognostic value of baseline PCT parameters, none of the parameters reached statistical significance (p > 0.05); only PS showed a weak trend toward lower values in the responder group.
Conclusion: CT perfusion parameters reflect tissue vascularization and can serve as objective quantitative biomarkers of tumor response to neoadjuvant treatment. Baseline low PS values are associated with a likelihood of clinical response to preoperative chemotherapy in gastric cancer. Further studies with larger sample sizes are needed to clarify the prognostic role of PCT.
Congress Abstract
Oncology, Nuclear Medicine and Transplantology, 2(3, Suppl. 1), 2026, onmt_A19, https://doi.org/10.63946/onmt/19303
ABSTRACT:
Introduction: Sarcopenia is associated with an unfavorable prognosis in cancer patients, especially in the presence of concurrent nutritional deficiency risk. CT-based sarcopenia assessment remains the gold standard for non-invasive evaluation of muscle mass; however, its routine use is limited by the high labor intensity of manual muscle segmentation on CT images.
Objective: To determine the prognostic value of preoperative CT-based sarcopenia assessment, performed using a developed software assistant, as a predictor of postoperative complications in patients with gastric and pancreatic tumors.
Materials and Methods: The study was conducted in two stages. At the first stage, a muscle tissue segmentation model was trained on 610 CT images (Dice coefficient on the training set — 0.95). A program was developed as an integrated information system incorporating computer vision algorithms, which identifies a single axial slice at the L3 level and performs subsequent semantic segmentation using convolutional neural networks. Thus, the muscle tissue area at the L3 vertebral level was automatically calculated with adjustment for the square of the patient's height, and the skeletal muscle index (SMI) was computed.
At the second stage, using this software module, sarcopenia was assessed preoperatively in 65 patients with gastric cancer and 55 patients with pancreatic cancer who subsequently underwent gastrectomy and pancreaticoduodenal resection, respectively. Sarcopenia was defined as SMI values of < 52.4 cm²/m² for men and < 38.5 cm²/m² for women. The severity of postoperative complications was assessed according to the Clavien–Dindo classification. Differences were considered statistically significant at p < 0.05.
Results: The prevalence of sarcopenia was evaluated in both groups: in patients with gastric cancer it was 77% (50 out of 65 patients), and in patients with pancreatic cancer – 73% (40 out of 55), indicating a considerable prevalence of this condition in this patient population.
The crude relative risk of overall postoperative complications (RR = 0.94; 95% CI 0.60–1.47; p > 0.05) and pancreatic fistulas in particular (RR = 0.64; 95% CI 0.33–1.26; p > 0.05) in pancreatic cancer patients with sarcopenia did not differ from that in patients without sarcopenia, indicating comparable complication rates in both groups.
In gastric cancer patients, the overall rate of postoperative complications also did not correlate with the presence of sarcopenia (p = 0.392); however, severe complications (≥ IIIb by Clavien–Dindo) were observed only in patients with sarcopenia (p < 0.001).
Conclusions: Thus, the inclusion of automated CT-based sarcopenia assessment in preoperative workup may help identify a high-risk group of cancer patients for severe postoperative complications, enabling optimization of personalized management strategies. However, multivariate analysis accounting for other clinical factors is required; further studies with validation on larger cohorts are necessary to justify the implementation of this method into clinical practice.
Objective: To determine the prognostic value of preoperative CT-based sarcopenia assessment, performed using a developed software assistant, as a predictor of postoperative complications in patients with gastric and pancreatic tumors.
Materials and Methods: The study was conducted in two stages. At the first stage, a muscle tissue segmentation model was trained on 610 CT images (Dice coefficient on the training set — 0.95). A program was developed as an integrated information system incorporating computer vision algorithms, which identifies a single axial slice at the L3 level and performs subsequent semantic segmentation using convolutional neural networks. Thus, the muscle tissue area at the L3 vertebral level was automatically calculated with adjustment for the square of the patient's height, and the skeletal muscle index (SMI) was computed.
At the second stage, using this software module, sarcopenia was assessed preoperatively in 65 patients with gastric cancer and 55 patients with pancreatic cancer who subsequently underwent gastrectomy and pancreaticoduodenal resection, respectively. Sarcopenia was defined as SMI values of < 52.4 cm²/m² for men and < 38.5 cm²/m² for women. The severity of postoperative complications was assessed according to the Clavien–Dindo classification. Differences were considered statistically significant at p < 0.05.
Results: The prevalence of sarcopenia was evaluated in both groups: in patients with gastric cancer it was 77% (50 out of 65 patients), and in patients with pancreatic cancer – 73% (40 out of 55), indicating a considerable prevalence of this condition in this patient population.
The crude relative risk of overall postoperative complications (RR = 0.94; 95% CI 0.60–1.47; p > 0.05) and pancreatic fistulas in particular (RR = 0.64; 95% CI 0.33–1.26; p > 0.05) in pancreatic cancer patients with sarcopenia did not differ from that in patients without sarcopenia, indicating comparable complication rates in both groups.
In gastric cancer patients, the overall rate of postoperative complications also did not correlate with the presence of sarcopenia (p = 0.392); however, severe complications (≥ IIIb by Clavien–Dindo) were observed only in patients with sarcopenia (p < 0.001).
Conclusions: Thus, the inclusion of automated CT-based sarcopenia assessment in preoperative workup may help identify a high-risk group of cancer patients for severe postoperative complications, enabling optimization of personalized management strategies. However, multivariate analysis accounting for other clinical factors is required; further studies with validation on larger cohorts are necessary to justify the implementation of this method into clinical practice.