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Cong R, Xu R, Ming J, Zhu Z. Construction of a preoperative nomogram model for predicting perineural invasion in advanced gastric cancer. Front Med (Lausanne) 2024; 11:1344982. [PMID: 38912337 PMCID: PMC11190154 DOI: 10.3389/fmed.2024.1344982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/24/2024] [Indexed: 06/25/2024] Open
Abstract
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.
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Affiliation(s)
- Ruochen Cong
- Department of Radiology, Nantong No. 1 People’s Hospital, Nantong, China
| | - Ruonan Xu
- Department of Radiology, Nantong No. 6 People’s Hospital, Nantong, China
| | - Jialei Ming
- Department of Radiology, Nantong No. 1 People’s Hospital, Nantong, China
| | - Zhengqi Zhu
- Department of Radiology, Nantong City Cancer Hospital, Nantong, China
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Li J, Yin H, Zhang H, Wang Y, Ma F, Li L, Gao J, Qu J. Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers. Acad Radiol 2024:S1076-6332(24)00243-5. [PMID: 38734580 DOI: 10.1016/j.acra.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P<0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P<0.05). CONCLUSION DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
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Lu W, Tan X, Zhong Y, Wang P, Ge Y, Zhang H, Hu S. Spectral CT in the evaluation of perineural invasion status in rectal cancer. Jpn J Radiol 2024:10.1007/s11604-024-01575-7. [PMID: 38709434 DOI: 10.1007/s11604-024-01575-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/15/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer. METHODS Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test. RESULTS The preoperative spectral CT parameters (40-70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%. CONCLUSION Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.
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Affiliation(s)
- Wenzheng Lu
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Xiaoying Tan
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Yanqi Zhong
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Peng Wang
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital, Jiangnan University, No.1000, Hefeng Road, Wuxi, Jiangsu, 214000, China.
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Yang Y, Yi Y, Wang Z, Li S, Zhang B, Sang Z, Zhang L, Cao Q, Li B. A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma. BMC Cancer 2024; 24:460. [PMID: 38609892 PMCID: PMC11015586 DOI: 10.1186/s12885-024-12239-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/09/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. METHODS We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). RESULTS We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. CONCLUSIONS By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.
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Affiliation(s)
- Yu Yang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yan Yi
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhongtang Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shanshan Li
- Department of Oncology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Bin Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zheng Sang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Lili Zhang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qiang Cao
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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Zhu Z, Gong H, Gu J, Dai Y, Yang C, Mao M, Song A, Feng F. Development and validation of a preoperative CT-based risk scoring system for predicting recurrence-free survival in patients undergoing curative surgery for gastric cancer. Eur J Radiol 2024; 171:111303. [PMID: 38215532 DOI: 10.1016/j.ejrad.2024.111303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/30/2023] [Accepted: 01/07/2024] [Indexed: 01/14/2024]
Abstract
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.
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Affiliation(s)
- Zhengqi Zhu
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Haipeng Gong
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Jianan Gu
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Yongfeng Dai
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Chunyan Yang
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Mimi Mao
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China
| | - Anyi Song
- Radiology Department, Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Feng Feng
- Radiology Department, Jiangsu Province Nantong City Cancer Hospital, Nantong 226300, Jiangsu Province, China.
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Wang G, Liu X, Zhou J. Differentiating gastric schwannoma from gastric stromal tumor (≤5 cm) by histogram analysis based on iodine-based material decomposition images: a preliminary study. Front Oncol 2023; 13:1243300. [PMID: 38044988 PMCID: PMC10691544 DOI: 10.3389/fonc.2023.1243300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Objective This study aims to investigate the value of histogram analysis based on iodine-based material decomposition (IMD) images obtained through dual-energy computed tomography (DECT) to differentiate gastric schwannoma (GS) from gastric stromal tumor (GST) (≤5 cm) preoperatively. Methods From January 2015 to January 2023, 15 patients with GS and 30 patients with GST (≤5 cm) who underwent biphasic contrast-enhanced scans using DECT were enrolled in this study. For each tumor, we reconstructed IMD images at the arterial phase (AP) and venous phase (VP). Nine histogram parameters were automatically extracted and selected using MaZda software based on the IMD of AP and VP, respectively, including mean, 1st, 10th, 50th, 90th, and 99th percentile of the iodine concentration value (Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99), variance, skewness, and kurtosis. The extracted IMD histogram parameters were compared using the Mann-Whitney U-test. The optimal IMD histogram parameters were selected using receiver operating characteristic (ROC) curves. Results Among the IMD histogram parameters of AP, the mean, Perc.50, Perc.90, Perc.99, variance, and skewness of the GS group were lower than that of the GST group (all P < 0.05). Among the IMD histogram parameters of VP, Perc.90, Perc.99, and the variance of the GS group was lower than those of the GST group (all P < 0.05). The ROC analysis showed that Perc.99 (AP) generated the best diagnostic performance with the area under the curve, sensitivity, and specificity being 0.960, 86.67%, and 93.33%, respectively, when using 71.00 as the optimal threshold. Conclusion Histogram analysis based on IMD images obtained through DECT holds promise as a valuable tool for the preoperative distinction between GS and GST (≤5 cm).
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Affiliation(s)
- Gang Wang
- Department of Radiology, Lanzhou University First Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Xianwang Liu
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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