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Background: Lymph node (LN) metastasis is a significant prognostic factor for esophageal squamous cell carcinoma (ESCC), and there are no satisfactory methods for accurately predicting metastatic LNs. The present study aimed to assess the efficacy of 99mTc‐3PRGD2 single‐photon emission computed tomography (SPECT)/computed tomography (CT) for diagnosing metastatic LNs in ESCC. Methods: A total of 15 enrolled patients with ESCC underwent 99mTc‐3PRGD2 SPECT/CT and 18F‐fluorodeoxyglucose positron emission tomography‐computed tomography (18F‐FDG PET/CT) examinations preoperatively. High‐definition bone carving reconstruction technology (HD‐xSPECT Bone) was applied to quantitatively assess the LN's SUVmax via SPECT/CT. The two methods were compared for diagnosing metastatic LNs with pathology as the gold standard. Results: Among 15 patients, 23 metastatic lymph node stations (mLNSs) were predicted by SPECT/CT, with a mean SUVmax of 2.71 ± 1.34, of which 15 were pathologically confirmed; 32 mLNSs were predicted by PET/CT with a mean SUVmax of 4.41 ± 4.02, of which 17 were pathologically confirmed. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of SPECT/CT for diagnosing metastatic LNs were 62.50%, 91.30%, 85.34%, 65.22%, and 90.32%, respectively, and those of PET/CT were 70.83%, 83.70%, 81.03%, 53.13%, and 91.67%, respectively. There was no significant difference in sensitivity (p = 0.061) or specificity (p = 0.058) between the two methods. The AUCSPECT/CT was 0.816 and the SUVmax threshold was 2.5. Conclusion: 99mTc‐3PRGD2 SPECT/CT might be an effective method for diagnosing metastatic LNs in ESCC, especially in combination with HD‐xSPECT Bone. The diagnostic efficiency of this method was noninferior to that of 18F‐FDG PET/CT. The SUVmax threshold of 2.5 showed the highest agreement with the pathology findings.This study utilized 99mTc‐3PRGD2 SPET/CT imaging in combination with high‐definition bone carving reconstruction technology to propose a novel method for diagnosing metastatic lymph nodes in esophageal squamous cell carcinoma. This approach allows for the integration of targeted imaging and quantitative analysis of lesions to assess metastatic lymph nodes. The diagnostic efficiency of this method was comparable to that of 18F‐FDG PET/CT. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients. Methods: This study establishes a CRC immune risk model based on CD8+ T cell-related genes. CD8+ T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8+ T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm. Results: These genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model's prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan–Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8+ T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model's efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect. Conclusions: This study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8+ T-cell infiltration. [ABSTRACT FROM AUTHOR]

It has been a big challenge to distinguish synchronous multiple primary lung cancer (sMPLC) from primary lung cancer with intrapulmonary metastases (IPM). We aimed to assess the clinical application of dynamic 18F-FDG PET/CT in patients with multiple lung cancer nodules. We enrolled patients with multiple pulmonary nodules who had undergone dynamic 18F-FDG PET/CT and divided them into sMPLC and IPM groups based on comprehensive features. The SUVmax, fitted K i value based on dynamic scanning, and corresponding maximum diameter ( D max ) from the two largest tumors were determined in each patient. We determined the absolute between-tumor difference of SU V max / D max and K i / D max (Δ SU V max / D max ; Δ K i / D max ) and assessed the between-group differences. Further, the diagnostic accuracy was evaluated by ROC analysis and the correlation between Δ SU V max / D max and Δ K i / D max from all groups was determined. There was no significant difference for Δ SU V max / D max between the IPM and sMPLC groups, while the IPM group had a significantly higher Δ K i / D max than the sMPLC group. The AUC of Δ K i / D max for differentiating sMPLC from IPM was 0.80 (cut-off value of K i = 0.0059 , sensitivity 79%, specificity 75%, p < 0.001). There was a good correlation (Pearson r = 0.91 , 95% CI: 0.79-0.96, p < 0.0001) between Δ SU V max / D max and Δ K i / D max in the IPM group but not in the sMPLC group (Pearson r = 0.45 , p > 0.05). Dynamic 18F-FDG PET/CT could be a useful tool for distinguishing sMPLC from IPM. K i calculation based on Patlak graphic analysis could be more sensitive than SUVmax in discriminating IPM from sMPLC in patients with multiple lung cancer nodules. [ABSTRACT FROM AUTHOR]

It has been a big challenge to distinguish synchronous multiple primary lung cancer (sMPLC) from primary lung cancer with intrapulmonary metastases (IPM). We aimed to assess the clinical application of dynamic 18F-FDG PET/CT in patients with multiple lung cancer nodules. We enrolled patients with multiple pulmonary nodules who had undergone dynamic 18F-FDG PET/CT and divided them into sMPLC and IPM groups based on comprehensive features. The SUVmax, fitted K i value based on dynamic scanning, and corresponding maximum diameter ( D max ) from the two largest tumors were determined in each patient. We determined the absolute between-tumor difference of SU V max / D max and K i / D max (Δ SU V max / D max ; Δ K i / D max ) and assessed the between-group differences. Further, the diagnostic accuracy was evaluated by ROC analysis and the correlation between Δ SU V max / D max and Δ K i / D max from all groups was determined. There was no significant difference for Δ SU V max / D max between the IPM and sMPLC groups, while the IPM group had a significantly higher Δ K i / D max than the sMPLC group. The AUC of Δ K i / D max for differentiating sMPLC from IPM was 0.80 (cut-off value of K i = 0.0059 , sensitivity 79%, specificity 75%, p < 0.001). There was a good correlation (Pearson r = 0.91 , 95% CI: 0.79-0.96, p < 0.0001) between Δ SU V max / D max and Δ K i / D max in the IPM group but not in the sMPLC group (Pearson r = 0.45 , p > 0.05). Dynamic 18F-FDG PET/CT could be a useful tool for distinguishing sMPLC from IPM. K i calculation based on Patlak graphic analysis could be more sensitive than SUVmax in discriminating IPM from sMPLC in patients with multiple lung cancer nodules. [ABSTRACT FROM AUTHOR]

Background: Currently, patients with cT1 stage non-small cell lung cancer measuring less than 2 cm are recommended to undergo sublobar resection. However, it should be noted that there is tumor heterogeneity within these lung nodules. Potential visceral pleural invasion (VPI) is regarded as a significant factor that contributes to local recurrence and poorer prognosis after sublobar resection and postoperative upstaging of the T-stage. Currently, there are no effective techniques for preoperative or intraoperative prediction of the status of VPI in lung nodules. The primary objective of this study is to develop a machine learning model for the non-invasive prediction of VPI, thereby providing surgical decision-making support for surgeons during operations., Methods: A total of 983 patients with nodules located within 5 mm distance from the pleura were included. Machine learning models were developed utilizing preprocessed 2D, 2.5D, and 3D computed tomography (CT) imaging data. These models were employed to predict the status of VPI, leveraging radiomics and deep learning techniques. The aforementioned three groups of data were further categorized into region of interest (ROI)-only (exclusively focused on the ROI) and ROI-rect (the minimum bounding rectangle or cuboid of the ROI) groups, based on whether they included images outside the ROI. Receiver operating characteristic (ROC) curves were created for the assessment of predictive accuracy across different models. Employing the Youden's index, patients were categorized into high-risk and low-risk groups based on the model's criteria, which was then followed by an in-depth analysis of overall survival rates among the distinct patient cohorts., Results: This study included a training cohort of 786 patients and a validation cohort of 197 patients. In comparison to radiomic and radiological models, deep learning models, especially the 2D-rect model, demonstrated better predictive performance. Although the 3D-ROI-only model exhibited the highest areas under the curve (AUC) value (0.952), its predictive performance for the status of VPI was found to be inferior according to the decision curve, calibration curve, and survival analysis., Conclusions: The developed deep learning signature offers a robust instrument for the precise prognostication of vascular invasion in stage cT1 lung adenocarcinoma, thereby enhancing stratification for prognostic evaluation. Moreover, the application of this advanced computational model aids in the refinement of therapeutic approach formulation for individuals diagnosed with cT1 lung adenocarcinoma., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-601/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)

Background: Lymph node (LN) metastasis is a significant prognostic factor for esophageal squamous cell carcinoma (ESCC), and there are no satisfactory methods for accurately predicting metastatic LNs. The present study aimed to assess the efficacy of 99m Tc-3PRGD 2 single-photon emission computed tomography (SPECT)/computed tomography (CT) for diagnosing metastatic LNs in ESCC., Methods: A total of 15 enrolled patients with ESCC underwent 99m Tc-3PRGD 2 SPECT/CT and 18F-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 F-FDG PET/CT) examinations preoperatively. High-definition bone carving reconstruction technology (HD-xSPECT Bone) was applied to quantitatively assess the LN's SUV max via SPECT/CT. The two methods were compared for diagnosing metastatic LNs with pathology as the gold standard., Results: Among 15 patients, 23 metastatic lymph node stations (mLNSs) were predicted by SPECT/CT, with a mean SUV max of 2.71 ± 1.34, of which 15 were pathologically confirmed; 32 mLNSs were predicted by PET/CT with a mean SUV max of 4.41 ± 4.02, of which 17 were pathologically confirmed. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of SPECT/CT for diagnosing metastatic LNs were 62.50%, 91.30%, 85.34%, 65.22%, and 90.32%, respectively, and those of PET/CT were 70.83%, 83.70%, 81.03%, 53.13%, and 91.67%, respectively. There was no significant difference in sensitivity (p = 0.061) or specificity (p = 0.058) between the two methods. The AUC SPECT/CT was 0.816 and the SUV max threshold was 2.5., Conclusion: 99m Tc-3PRGD 2 SPECT/CT might be an effective method for diagnosing metastatic LNs in ESCC, especially in combination with HD-xSPECT Bone. The diagnostic efficiency of this method was noninferior to that of 18 F-FDG PET/CT. The SUV max threshold of 2.5 showed the highest agreement with the pathology findings., (© 2024 The Author(s). Thoracic Cancer published by John Wiley & Sons Australia, Ltd.)

Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-617/coif). TH serves as an unpaid editorial board member of Translational Lung Cancer Research from January 2022 to December 2023. AR serves as an unpaid editorial board member of Translational Lung Cancer Research from November 2020 to October 2022. RAR serves as an unpaid editorial board member of Translational Lung Cancer Research from January 2022 to December 2023. MP serves as an unpaid editorial board member of Translational Lung Cancer Research from October 2021 to September 2023. RAR has been a consultant for Amgen, Ipsen Biopharmaceuticals, Novartis, Advanced Accelerator Applications, Curium Pharma, EMD Serono, Astra-Zeneca. He has also received research funding (to institution) from Merck & Co and Aadi Biosciences. He is also on the speaker bureaus for Ipsen Biopharmaceuticals and Astra-Zeneca. Mariano Provencio received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events and consulting fees from BMS, Astra Zéneca, MSD, Roche, Takeda and Pfizer. Jay M. Lee reports advisory board/consultant of Astrazeneca, Bristol Myers Squibb, Genentech, Novartis, Roche; research support from Genentech, Novartis; steering committee of Genentech, Novartis; speaker’s bureau of AstraZeneca, Genentech, Roche; stock from Moderna; patents of UCLA. Antonio Passaro received honoraria for consulting, advisory role or lectures from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Jansenn, Merck Sharp & Dohme, Roche Genentech, Novartis, MundiPharma, Daiichi Sankyo, eCancer and Medscape. TC reports speaker fees/honoraria from The Society for Immunotherapy of Cancer, Bristol Myers Squibb, Roche, Medscape, and PeerView; travel, food and beverage expenses from Dava Oncology; advisory role/consulting fees from AstraZeneca, Bristol Myers Squibb, EMD Serono, Merck & Co., Genentech, and Arrowhead Pharmaceuticals; and institutional research funding from AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim and EMD Serono. The other authors have no conflicts of interest to declare.

Background: Gastric cancer (GC) is one of the most common digestive tract tumors, and a serious threat to human health. Long non-coding RNA (lncRNA) are involved in many cancers. However, the biological functions of most lncRNAs are unclear. In this study, we investigated the mechanisms by which FLVCR1-AS1 regulated GC progression., Methods: FLVCR1-AS1 expression in GC tissues and 3 GC cell lines were measured by quantitative real-time PCR (qRT-PCR). Invasion, proliferation, and apoptosis profiles were analyzed by commercial assays to determine the biological functions of FLVCR1-AS1 in GC cells. The binding sites of micro RNA-155 (miR-155) on FLVCR1-AS1 were predicted using the miRDB program. Luciferase reporter assay was used to validate direct targeting of FLVCR1-AS1 by miR-155. The effects of FLVCR1-AS1 on expressions of c-Myc and p21 were assessed by western blotting. In vivo experiments were performed to analyze the effects of FLVCR1-AS1 on GC tumor growth., Results: High expression of FLVCR1-AS1 correlated with poor clinical outcomes and prognosis in patients with GC. FLVCR1-AS1 promoted proliferation and invasion of GC cells by acting as a ceRNA to sponge miR-155., Conclusion: FLVCR1-AS1 acted as an oncogene in GC via FLVCR1-AS1-miR-155-c-Myc signaling and may serve as a novel therapeutic target for treatment of patients with GC., Competing Interests: None.