Lymph Node Metastasis Status Research Articles

Identification of a Novel Vascular Endothelial Growth Factor Receptor-3-Targeting Peptide for Molecular Imaging of Metastatic Lymph Nodes.

Because of the insidious nature of lymphatic metastatic cancer, accurate imaging tracing is very difficult to achieve in the clinic. Previous studies have developed the LARGR peptide (named TMVP1) as a radiotracer for vascular endothelial growth factor receptor-3 (VEGFR-3) imaging in cancer. However, its affinity for the target remains insufficient, resulting in low imaging sensitivity. In this study, we identified a high-affinity VEGFR-3 targeting peptide, named TMVP1446, using a multiplex screening platform. TMVP1446 demonstrated a dissociation constant of 8.97 × 10-8 M. Both in vitro and in vivo assays confirmed that fluorescently labeled TMVP1446 specifically bound to VEGFR-3. In a 4T1-luciferase tumor mouse model, cyanine 7-labeled TMVP1446 effectively discriminated between contralateral normal lymph nodes (c-LN) and cancer-metastatic sentinel lymph nodes (m-SLN). To evaluate the potential of TMVP1446, we developed a novel VEGFR-3 positron emission tomography radiotracer ([68Ga]Ga-DOTA-TMVP1446) for cancer-m-SLN imaging. [68Ga]Ga-DOTA-TMVP1446 accurately detected and assessed the status of lymph node metastasis, even in micrometastatic tumors, in the B16-F10 mouse tumor model. These findings suggest that TMVP1446 has great potential for advancing VEGFR-3 molecular imaging and metastatic sentinel lymph node imaging.

A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer

A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.

In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM is necessary and holds significant clinical importance. We develop a ranking attention multiple instance learning (RA-MIL) model that integrates convolutional neural networks (CNNs) and ranking attention pooling to diagnose LNM from T2 MRI. Our RA-MIL model applies the CNNs to derive imaging features from 2D MRI slices and employs ranking attention pooling to create patient-level feature representation for diagnostic classification. Based on the MIL and attention theory, informative regions of top-ranking MRI slices from LNM-positive patients are visualized to enhance the interpretability of automatic LNM prediction. This retrospective study collected 300 female patients with cervical cancer who underwent T2-weighted magnetic resonance imaging (MRI) scanning and histopathological diagnosis from one hospital (289 patients) and one open-source dataset (11 patients). Our RA-MIL model delivers promising LNM prediction performance, achieving the area under the receiver operating characteristic curve (AUC) of 0.809 on the internal test set and 0.833 on the public dataset. Experiments show significant improvements in LNM status prediction using the proposed RA-MIL model compared with other state-of-the-art (SOTA) comparative deep learning models. The developed RA-MIL model has the potential to serve as a non-invasive auxiliary tool for preoperative LNM prediction, offering visual interpretability regarding informative MRI slices and regions in LNM-positive patients.

Potential Oncogenic and Immunosuppressive Roles of Multiple EGF-Like Domains 6 (MEGF6) in Colon Cancer: Insights from Bioinformatic Analysis

Multiple epidermal growth factor-like domains protein 6 (MEGF6) is a high molecular weight protein with several EGF-like domains. Although it has been demonstrated to promote metastasis and chemoresistance in colon cancer cells, its specific oncogenic and immunologic functions remain largely unexplored. This study aims to comprehensively analyze publicly available datasets to define the role of MEGF6 in colon cancer development and immune response modulation. The expression of MEGF6 in colon adenocarcinoma (COAD) and normal tissues at both mRNA and protein levels was assessed using the GEPIA2 and HPA databases, respectively. The UALCAN database was employed to examine the association between MEGF6 expression and clinicopathological outcomes in COAD patients, and survival analysis was conducted using the KM plotter database. Functional enrichment analysis of MEGF6-correlated genes was performed using the ShinyGO online tool. Additionally, the correlation of MEGF6 with immune cell infiltration and immunosuppressive molecules was explored through the TIMER and TISIDB databases. Our analysis revealed significantly higher mRNA and protein expressions of MEGF6 in COAD tissues compared to normal tissues, with associations to cancer stage, nodal metastasis status, and histological subtype. High MEGF6 expression was correlated with poorer survival outcomes, and genes correlated with MEGF6 were enriched in biological processes related to cellular component organization, cell adhesion, and extracellular matrix interaction. Furthermore, MEGF6 expression demonstrated a significant correlation with immune cell infiltration and the expression of immunosuppressive molecules, particularly CTLA-4, PD-1, PD-L1, TGF-β, and IL-10. In conclusion, our findings suggest that MEGF6 may play an oncogenic role by influencing cell and extracellular matrix interactions. Its overexpression may indicate immunosuppressive properties within the cancer microenvironment. Therefore, MEGF6 holds promise as a potential biomarker for predicting both prognosis and therapeutic responses in colon cancer. HIGHLIGHTS MEGF6 has been shown to involve colon cancer growth and chemotherapeutic resistance. Bioinformatics confirmed a prognostic significance of MEGF6 in colon cancer. MEGF6 may exert its oncogenic effects by influencing cell-matrix interactions. MEGF6 expression is correlated with immunosuppressive properties of cancers. GRAPHICAL ABSTRACT

ABO Blood Type and Pretreatment Systemic Inflammatory Response Index Associated with Lymph Node Metastasis in Patients with Breast Cancer.

Lymph node metastasis (LNM) is an important prognostic factor for breast cancer. Inflammatory stimulation can change tumor microenvironment and lead to LNM, but the relationship between LNM and peripheral immunoinflammatory indices has not been clarified in breast cancer. The clinical information of 1918 patients with breast cancer admitted to Meizhou People's Hospital from October 2017 to December 2023 were retrospectively analyzed. The relationship of clinicopathological features (age, body mass index (BMI), ABO blood types, family history of cancer, tumor site, disease stage, LNM, distant metastasis, and molecular subtypes) and peripheral immunoinflammatory indices (pan-immune inflammation value (PIV), systemic immune inflammation index (SII), and system inflammation response index (SIRI)) were analyzed. There were 935 (48.7%) patients had no LNM and 983 (51.3%) had LNM. There were statistically significant differences in the distributions of ABO blood groups (p=0.022) and molecular subtypes (p<0.001) between the two groups. PIV, SII, and SIRI levels in patients with LNM were significantly higher than those without LNM (all p<0.05). The proportions of LNM in patients with high PIV, SII, and SIRI levels were higher than those with low PIV, SII, and SIRI levels, respectively. Logistic regression analysis showed that non-O blood type (non-O blood type vs O blood type, odds ratio (OR): 1.327, 95% confidence interval (CI): 1.056-1.667, p=0.015), luminal B subtype (luminal B vs luminal A, OR: 2.939, 95% CI: 2.147-4.022, p<0.001), HER2+ subtype (HER2+ vs luminal A, OR: 2.044, 95% CI: 1.388-3.009, p<0.001), and high SIRI level (≥0.875 vs <0.875, OR: 1.572, 95% CI: 1.092-2.265, p=0.015) were independently associated with LNM. Non-O blood type, luminal B and HER2+ subtypes, and high SIRI level (≥0.875) have potential role in predicting the status of LNM in breast cancer patients.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited. This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging. This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT-based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT-based DTL signature was combined with independent clinical predictors to construct a ViT-based deep transfer learning nomogram (DTLN). The ViT-based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775-0.867) in the training cohort and 0.825 (95% CI, 0.758-0.891) in the validation cohort. The ViT-based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT-based DTL signature was then used to construct ViT-based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827-0.903) and 0.894 (95% CI, 0845-0942), respectively, surpassing both the clinical factor model and the ViT-based DTL model (p<0.001). This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

Role of Ultrasonographic and Clinicopathological Characteristics in Assessing Stromal Tumor-Infiltrating Lymphocyte Density in Triple-Negative Breast Cancer.

A high density of stromal tumor-infiltrating lymphocytes (sTILs) is positively correlated with the pathological complete response rate and favorable survival in patients with triple-negative breast cancer (TNBC). Heterogeneity in stromal lymphocyte distribution and limited tumor sampling may affect the accuracy of sTIL quantification in biopsy samples from patients receiving neoadjuvant therapy. Thus, identifying additional biomarkers to complement sTIL evaluation is essential. To identify biomarkers that could be used to complement sTILs evaluation. Retrospective cohort study. A total of 162 patients with invasive TNBC were enrolled in the study. The following data were gathered: sTIL density, Ki67, nuclear grades of cancer cells, lymphovascular invasion status, the American Joint Committee on Cancer stage, axillary lymph node metastasis status, and ultrasonographic parameters of the tumor (size, shape, orientation, margin, internal echo pattern, posterior feature, and vascularity). The relationship between sTIL density and ultrasonographic or clinicopathological characteristics was investigated using both continuous and categorical analyses. Posterior features of the primary tumors was associated with sTIL density (p = 0.038). Additionally, the Ki67 levels and nuclear grades were also associated with sTIL density (p < 0.001 and p = 0.024, respectively). When stratified according to a 20% cut-off of sTIL density, the posterior features of primary tumors, Ki67 levels, and nuclear grades significantly differed between the high and low sTIL density tumors. Tumors with high Ki67 levels were more likely to exhibit high sTIL density than low sTIL density [odds ratio (OR): 2.75, p = 0.021]. Furthermore, nuclear grade III tumors demonstrated significantly higher sTIL density than nuclear grade I-II tumors (OR: 2.49, p = 0.014). Additionally, tumors with posterior enhancement or no posterior features were more likely to exhibit high sTIL density than tumors with acoustic shadows (OR: 2.91, p = 0.028; OR: 2.74, p = 0.022, respectively). Low sTIL density is frequently observed in tumors exhibiting acoustic shadows on ultrasound. However, high sTIL density is more common in tumors with posterior enhancement or no posterior features. Furthermore, high Ki67 levels (> 40%) and high nuclear grades are positively correlated with high sTIL density. Our study findings highlight the need for closer surveillance of these biomarkers to complement sTIL evaluation in TNBC.

Advanced diffusion-weighted MRI models for preoperative prediction of lymph node metastasis in resectable gastric cancer.

To investigate the potential of six advanced diffusion-weighted imaging (DWI) models for preoperative prediction of lymph node metastasis (LNM) in resectable gastric cancer (GC). Between Nov 2022 and Nov 2023, standard MRI scans were prospectively performed in consecutive patients with endoscopic pathology-confirmed gastric adenocarcinoma who were referred for direct radical gastrectomy. Six DWI models, including fractional order calculus (FROC), continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), the mono-exponential model (MEM) and the stretched exponential model (SEM) were computed. Surgical pathologic diagnosis of LNM was the reference standard, and patients were classified into LNM-positive or LNM-negative groups accordingly. The morphological features and quantitative parameters of the DWI models in different LNM categories were analyzed and compared. Multivariable logistic regression was used to screen significant predictors. Receiver-operating characteristic curves and the area under the curve (AUC) were plotted to evaluate the performances, the Delong test was performed to compare the AUCs. In the LNM-positive group, tumor thickness and kurtosis (DKI_K) were significantly higher, while anomalous diffusion coefficient (CTRW_D), diffusivity (DKI_D), diffusion coefficient (FROC_D), pseudodiffusion coefficient (IVIM_D*), perfusion fraction (IVIM_f), and ADC were lower compared to the LNM-negative group. Clinical tumor staging (cT) and CTRW_D were independent predictors. Their combination demonstrated a superior AUC of 0.930, significantly higher than that of individual parameters. Tumor thickness, DKI_K, CTRW_D, DKI_D, FROC_D, IVIM_D*, IVIM_f and ADC were associated with LNM status. The combination of independent predictors of cT and CTRW_D further enhanced the performance.

Comparison of Ultrasound-Guided Percutaneous Microwave Ablation and Conventional Thyroidectomy in the Treatment of Papillary Thyroid Microcarcinoma: Meta-Analysis.

Purpose: To perform a meta-analysis of clinical trials comparing percutaneous microwave ablation (PMWA) guided by ultrasound with conventional thyroidectomy for the management of papillary thyroid microcarcinoma (PTMC), analyzing feasibility, safety, and long-term efficacy, and to provide clinical guidance for the treatment selection of PTMC. Methods: Embase, PubMed, Cochrane Library, Web of Science, CNKI, VIP Database, and Wanfang Database were systematically searched to identify clinical studies of PMWA or thyroidectomy for PTMC up to December 2023. The relevant data from the articles were extracted, and the data analysis was performed using RevMan 5.4 software. Results: A total of 442 articles were identified and subsequently screened based on the inclusion and exclusion criteria, 9 clinical studies involving a total of 1577 patients were included, with 788 patients in the PMWA group and 789 patients in the surgery group. Following data extraction and statistical analysis, in comparison to the surgery group, the PMWA group had shorter operation time [mean differences (MD) = -36.36; 95% CI -55.66 to -17.06; P = .0002], shorter hospital stay (MD = -3.93; 95% CI -5.55 to -2.30; P < .00001), less intraoperative bleeding (MD = -21.25; 95% CI -27.36 to -15.15; P < .00001), and lower hospital costs (MD = -1.00; 95% CI -1.33 to -0.66; P < .00001), all with statistical significance. The comparison of postoperative complications revealed a lower incidence of complications in the PMWA group compared to the surgery group [relative risk (RR) = 0.29; 95% CI 0.21 to 0.40; P < .00001], with statistical significance. Thyroid-related hormone analysis showed that the free triiodothyronine (MD = 0.61; 95% CI 0.33 to 0.90; P < .00001) and free thyroxine (MD = 1.81; 95% CI 0.94 to 2.68; P < .0001) levels in the PMWA group were higher than those in the traditional surgery group, while the levels of thyroid-stimulating hormone were lower than those in the traditional surgery group (MD = -7.63; 95% CI -10.25 to -5.01; P < .00001), with statistically significant differences, indicating that PMWA had a smaller impact on thyroid function. In 2 studies, there were no statistically significant disparities in postoperative recurrence or lymph node metastasis (LNM) between the 2 cohorts (RR = 0.70; 95% CI 0.33 to 1.50; P = .36). There were no statistically significant differences in physiological health score between different groups and different time points before and after treatment (P > .05); However, the mental component score and the total score of the Medical Outcomes Study (MOS) item short-form health survey were significantly elevated in the ablation group compared to the excision group post-treatment (P < .05). Conclusion: For patients with low-risk PTMC with definitive diagnosis and precise risk stratification, PMWA can be selected. PMWA treatment for patients with PTMC is comparable to conventional surgical treatment and has the advantages of minimal trauma, rapid recovery, no scarring, and fewer complications, which are superior to open surgery to a certain extent. For patients with ambiguous preoperative diagnosis and uncertainty regarding LNM status, surgical intervention is the optimal choice.

Enhancing lymph node metastasis prediction in adenocarcinoma of the esophagogastric junction: A study combining radiomic with clinical features.

The incidence of adenocarcinoma of the esophagogastric junction (AEJ) is increasing, and with poor prognosis. Lymph node status (LNs) is particularly important for planning treatment and evaluating the prognosis of patients with AEJ. However, the use of radiomic based on enhanced computed tomography (CT) to predict the preoperative lymph node metastasis (PLNM) status of the AEJ has yet to be reported. We sought to investigate the value of radiomic features based on enhanced CT in the accurate prediction of PLNM in patients with AEJ. Clinical features and enhanced CT data of 235 patients with AEJ from October 2017 to May 2023 were retrospectively analyzed. The data were randomly assigned to the training cohort (n=164) or the external testing cohort (n=71) at a ratio of 7:3. A CT-report model, clinical model, radiomic model, and radiomic-clinical combined model were developed to predict PLNM in patients with AEJ. Univariate and multivariate logistic regression were used to screen for independent clinical risk factors. Least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomic features. Finally, a nomogram for the preoperative prediction of PLNM in AEJ was constructed by combining Radiomics-score and clinical risk factors. The models were evaluated by area under the receiver operating characteristic curve (AUC-ROC), calibration curve, and decision curve analyses. A total of 181 patients (181/235, 77.02%) had LNM. In the testing cohort, the AUC of the radiomic-clinical model was 0.863 [95% confidence interval (CI)=0.738-0.957], and the radiomic model (0.816; 95% CI=0.681-0.929), clinical model (0.792; 95% CI=0.677-0.888), and CT-report model (0.755; 95% CI=0.647-0.840). The radiomic-clinical model is a feasible method for predicting PLNM in patients with AEJ, helping to guide clinical decision-making and personalized treatment planning.

Up-regulated DSG2 promotes tumor growth and reduces immune infiltration in cervical cancer

BackgroundDesmoglein-2 (DSG2) has been reported to play pivotal roles in various diseases. However, its roles in cervical cancer (CC) remain insufficiently elucidated. Here, we aimed to comprehensively explore the functional mechanisms of DSG2 in CC using bioinformatics and experimental methods. MethodsSeveral online databases, including Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE, LinkedOmics, MetaScape, Human protein atlas (HPA), OMICS and single-cell RNA sequencing (scRNA-seq) data were used to explore the expression, prognosis, gene mutations, and potential signaling pathway of DSG2 in CC. Quantitative real-time PCR (qRT-PCR) and western blotting were used to measure DSG2 expression in collected samples. Experimental assays were conducted to verify the effects of dysregulated DSG2 on cervical cell lines in vitro. ResultsBioinformatic analyses revealed that DSG2 was significantly up-regulated in CC compared to normal cervical tissues at both mRNA and protein levels. Elevated DSG2 levels were also associated with poor prognosis and clinical parameters (e.g., cancer stages, tumor grade, nodal metastasis status, etc.). DSG2 expression was predominantly observed in epithelial cells, increasing with disease progression on a single-cell resolution. Additionally, up-regulation of DSG2 significantly enhanced tumor purity by reducing the infiltration of immune cells (e.g., B cells, T cells, NK cells, etc.). Over-expression of DSG2 was further validated in collected CC samples at both mRNA and protein levels. Knockdown of DSG2 markedly reduced the proliferation and invasion of CC cell lines in vitro. ConclusionsIn summary, elevated levels of DSG2 were significantly associated with poor prognosis and diminished immune infiltration in CC. Thus, DSG2 may serve as a potential therapeutic and diagnostic biomarker for CC.

Lateral pelvic lymph nodes dissection of rectal neuroendocrine neoplasms: A prospective case-series and literature review

BackgroundRectal neuroendocrine neoplasms are relatively rare. Patients with rectal neuroendocrine neoplasms undergoing radical surgery have a higher rate of lymph node metastases. Robust evidence on the status of lateral pelvic lymph node metastases and the role of lateral pelvic lymph node dissection in those patients is lacking. This case-series study aimed to explore and address these issues. MethodsThis single-center, prospective case series consecutively enrolled patients with biopsy-proven rectal neuroendocrine neoplasms in a tertiary referral hospital between June 2022 and January 2024. All eligible patients underwent laparoscopic total mesorectal excision surgery and bilateral lateral pelvic lymph node dissection under general anesthesia. The clinicopathologic features, surgical outcomes, and postoperative complications were presented. The last follow-up was conducted in March 2024. ResultsA total of 11 patients with rectal neuroendocrine neoplasms—3 female and 8 male—were enrolled. The average age was 60.0 years (range, 53.5–65.5 years), and the median tumor size was 2.0 cm (range, 1.6–2.5 cm). Tumors invaded the muscularis propria in 7 patients. There were 3 cases of neuroendocrine tumor G1, 6 cases of neuroendocrine tumor G2, and 2 cases of neuroendocrine carcinoma. Among these patients, 11 (100.0%) had lymph node metastases, and 6 (54.5%) had lateral pelvic lymph node metastases. In addition, in 2 patients, only lateral pelvic lymph node metastases were observed, without involvement of the mesenteric lymph nodes. Five patients had tumors located on the left wall of the rectum, and only left-sided lateral pelvic lymph node metastases were observed. The other patient had both sides of lateral pelvic lymph node metastases due to circumferential growth of the tumor around the rectum. Anal preservation was achieved in all patients. The median operating time was 235.0 minutes (range, 210.5–335.5 minutes), and the median estimated blood loss was 50.0 mL (range, 45.0–75.0 mL). Two patients experienced postoperative dysuria and recovered spontaneously within 2–4 months after surgery. ConclusionOn the basis of a prospective case series, we demonstrate, for the first time, the lateral pelvic lymph node metastasis status in patients with rectal neuroendocrine neoplasms requiring radical total mesorectal excision surgery. Simultaneous bilateral lateral pelvic lymph node dissection may be a feasible and beneficial procedure for preventing local recurrence in these patients due to the lack of definitive neoadjuvant or adjuvant therapy options.

Expression and immune infiltration studies of IL-33-ST2-NF-κB signaling pathway in prostate cancer.

To analyze the expression of interleukin-33 (IL-33), growth-stimulated expression gene 2 (ST2), nuclear factor-kappaB (NF-κB) and immune cell infiltration in prostate cancer, this study aims to provide an experimental basis for the clinical prevention and treatment of prostate cancer. The expression of IL-33 in PCa tissues was analyzed using TCGA, TIMER and HPA databases. Using the UALCAN database, the systematic exploration of the relationship between IL-33 and various clinicopathological parameters was conducted. The correlation between IL-33 expression and immune cell infiltration was investigated using TIMER, CIBERSORT and GEPIA databases. To verify these analyses, 22 cases of normal prostate (NP), 76 cases of benign prostatic hyperplasia (BPH), and 100 cases of PCa were recruited. Immunohistochemical staining was performed to examine the expression of IL-33, ST2, NF-κB, and the infiltration of immune cells. Correlations between these factors were then determined. The expression of IL-33, ST2 and NF-κB was significantly lower in PCa tissues compared with NP (p < 0.05). IL-33 was not associated with age in PCa but showed associations with race, molecular characteristics, lymph node metastatic status, TP53 mutation and tumor grade. Furthermore, IL-33 was associated with immune cell infiltration. Positive correlations were observed between IL-33 and ST2 expressions, as well as between IL-33 and CD68+ macrophages in BPH and PCa. IL-33, ST2 and NF-κB are lowly expressed in PCa tissues, their expression decreases with the increasing malignancy of cancer. IL-33, ST2 and NF-κB are factors associated with PCa immune infiltration. IL-33 has an inhibitory effect on prostate cancer through the IL-33/ST2/NF-κB signalling pathway.

Arterial Mucosal Linear Enhancement at Contrast-enhanced MRI to Exclude Residual Tumor after Neoadjuvant Chemotherapy and Radiation Therapy for Rectal Cancer.

Background A watch-and-wait regimen for locally advanced rectal cancer after neoadjuvant chemotherapy and radiation therapy (NCRT) relies on identifying complete tumor response. However, the concordance between a complete response at combined T2-weighted and diffusion-weighted MRI (T2DWI) and pathologic complete response (pCR; ie, ypT0N0) in the tumor is unsatisfactory. Purpose To assess whether identification of mucosal linear enhancement (MLE) at arterial-phase contrast-enhanced (CE) T1-weighted MRI is associated with ypT0 status in patients with locally advanced rectal cancer after NCRT and to evaluate whether combining MLE at CE T1-weighted MRI and negative lymph node metastasis (LNM) at T2DWI can improve identification of pCR. Materials and Methods This retrospective study included patients with locally advanced rectal cancer who underwent total mesorectal excision after NCRT between July 2020 and July 2023 at a tertiary referral academic center. Restaging MRI included T2DWI and arterial-phase CE T1-weighted MRI for primary tumor assessment and T2DWI for evaluation of LNM status. Imaging features associated with ypT0 status were identified at multivariable regression analysis. Results In total, 239 patients (mean age, 58 years ± 12 [SD]; 180 male patients) were assessed. MLE was more common in the ypT0 group than in the ypT1-4 group after NCRT (73% vs 4%, respectively; P < .001). MLE was associated with higher odds of ypT0 status in an adjusted analysis (odds ratio, 137; 95% CI: 25, 767; P < .001). The combination of MLE and negative LNM status achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.88) for pCR. Conclusion MLE at CE MRI was associated with higher odds of complete tumor response. Combining MLE and negative LNM status showed good performance for identifying complete tumor response and may exclude residual tumors after NCRT in patients with locally advanced rectal cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Schoellnast in this issue.

Identification and expression of prognostic-related genes in kidney renal clear cell carcinoma and their possible regulatory mechanisms.

Many factors affect the prognosis of kidney renal clear cell carcinoma (KIRC). Early diagnosis can significantly improve the prognosis of KIRC patients. Therefore, a method needs to be developed to diagnose KIRC early, predict patient prognosis, and improve personalized treatments. The objective of this study is to utilize bioinformatics tools and public database resources to identify differentially expressed genes (DEGs) between renal cancer tissues and adjacent normal tissues, and to further screen for prognostic-related genes (PRGs) of KIRC. KIRC was studied using R language and FunRich software and several databases, including the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the University of Alabama at Birmingham cancer data analysis Portal (UALCAN), and Tumor Immune Estimation Resource (TIMER) databases. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression of multiple genes in KIRC and adjacent normal tissues. There were substantial differences in immune cell infiltration between the KIRC and adjacent normal tissues in the GSE40435 and GSE46699 datasets. In addition, we screened multiple PRGs of KIRC by combining the GEO and TCGA data. The UALCAN database verified that some representative PRGs were differently expressed depending on the lymph node metastasis status, grade, and stage of KIRC. The qRT-PCR results confirmed the expression of the PRGs in KIRC and adjacent normal tissues. Through the GO and KEGG analyses, interaction analysis, and TIMER database, we found that the prognosis of KIRC was closely related to immune microenvironment and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) signaling. Our findings could contribute to the prognosis prediction of KIRC, the selection of personalized treatments, and the early diagnosis of KIRC.

The impact of uncertainty resection on the prognosis of non-small cell lung cancer

Objective: To explore the impact of uncertain resection on postoperative survival in non-small cell lung cancer. Methods: This is a retrospective cohort study. A retrospective analysis was conducted on the data of 477 patients with non-small cell lung cancer who underwent lobectomy in the Department of Thoracic Surgery, the Fourth Hospital of Hebei Medical University from December 2012 to December 2013. There were 302 males and 175 females, aged (59±8) years (range: 27 to 79 years). According to the surgical resection criteria issued by the International Association for the Study of Lung Cancer, the patients were divided into the intact resection group (R0 group, 286 cases) and the uncertain resection group (R (un) group, 191 cases). Clinical data between the two groups were compared using χ2 test, and propensity score matching (PSM) was performed on patients using the R language, with matching variables including gender, age, smoking history, adjuvant therapy, TNM stage, pathological type, and tumor site. The nearest-neighbor method was used for 1∶3 matching and the caliper value was 0.02. The survival curve was plotted using the Kaplan-Meier method and compared using the Log-rank test. The Cox proportional hazards regression model was used to identify risk factors in overall survival (OS). Subgroup analysis was based on TNM staging and mediastinal lymph node metastasis status. Results: In the R (un) group, 68 patients had positive lymph in the highest group and 129 patients did not undergo complete dissection of the mediastinal lymph nodes. The baseline data for the R0 group and the R (un) group were corrected using PSM, and a total of 369 patients were successfully matched, including 227 cases in the R0 group and 142 cases in the R (un) group. After PSM, the 5-year survival rates of the R0 group and the R (un) group were 64.3% and 52.1%, respectively (P=0.021). The 5-year survival rates of stage Ⅰ, Ⅱ, and Ⅲ patients were 85.2%, 65.9%, and 34.8%, respectively (P<0.01). TNM stage (χ2=46.913, P<0.01), pathological classification of adenosquamous cell carcinoma (HR=5.970, 95% CI: 3.117 to 11.431, P<0.01) and R (un) resection (HR=1.512, 95% CI: 1.065 to 2.147, P=0.021) were prognostic factors for postoperative survival. Subgroup analysis showed that in stage Ⅲ patients, 5-year survival rates of the R0 group and the R (un) group after resection were 45.8% and 9.5%, respectively (P=0.002). Among patients with mediastinal lymph node metastasis, 5-year survival rates of the R0 group and the R (un) group were 50.6% and 7.1%, respectively (P<0.01). Conclusions: TNM staging, pathological type, and R (un) resection are prognostic factors for overall postoperative survival in non-small cell lung cancer. In stage Ⅰ and Ⅱ patients, R (un) is not a prognostic factor for postoperative survival of non-small cell lung cancer. In patients with stage Ⅲ and mediastinal lymph node metastasis, R (un) is a prognostic factor for non-small cell lung cancer after surgery.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer

BackgroundTo provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features.MethodsTwo cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features.ResultsPatients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77–0.91) and validation (AUC = 0.85, 95% CI = 0.73–0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features.ConclusionsMachine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

Effect of preoperative immune checkpoint inhibitors on reducing residual lymph node metastases in patients with gastric cancer: a retrospective study

Objective: To investigate the effect of immune checkpoint inhibitors on reducing residual lymph node metastasis in patients with gastric cancer. Methods: The cohort of this retrospective study comprised patients from Nanfang Hospital of Southern Medical University and the First Affiliated Hospital of Xiamen University who had undergone systemic treatment prior to gastrectomy with D2 lymphadenectomy and had achieved Grade 1 primary tumor regression (TRG1) from January 2014 to December 2023. After exclusion of patients who had undergone preoperative radiotherapy, data of 58 patients (Nanfang Hospital: 46; First Affiliated Hospital of Xiamen University: 12) were analyzed. These patients were allocated to preoperative chemotherapy (Chemotherapy group, N=36 cases) and preoperative immunotherapy plus chemotherapy groups (Immunotherapy group, N=22 cases). There were no significant differences between these groups in sex, age, body mass index, diabetes, tumor location, pathological type, Lauren classification, tumor differentiation, pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, mismatch repair protein status, number of preoperative treatment cycles, or duration of preoperative treatment (all P>0.05). The primary outcome measure was postoperative lymph node downstaging. Secondary outcomes included postoperative depth of invasion by tumor, number of lymph nodes examined, and factors affecting residual lymph node metastasis status. Results: Lymph node downstaging was achieved significantly more often in the Immunotherapy group than the Chemotherapy group (pN0: 90.9% [20/22] vs. 61.1% [22/36]; pN1: 4.5% [1/22] vs. 36.1% [13/36]; pN2: 4.5% [1/22) vs. 0; pN3: 0 vs. 2.8% [1/36], Z=-2.315, P=0.021). There were no significant difference between the two groups in number of lymph nodes examined (40.5±16.3 vs. 40.8±17.5, t=0.076, P=0.940) or postoperative depth of invasion by primary tumor (pT1a: 50.0% [11/22] vs. 30.6% [11/36]; pT1b: 13.6% [3/22] vs. 19.4% [7/36]; pT2: 13.6% [3/22] vs. 13.9% [5/36]; pT3: 13.6% [3/22] vs. 25.0% [9/36]; pT4a: 9.1% [2/22] vs. 11.1% [4/36], Z=-1.331, P=0.183). Univariate analysis revealed that both preoperative treatment regimens were associated with residual lymph node metastasis status in patients whose primary tumor regression was TRG1 (χ2=6.070, P=0.014). Multivariate analysis incorporated the following factors: pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, number of preoperative treatment cycles, and preoperative treatment duration. We found that a combination of immunotherapy and chemotherapy administered preoperatively was an independent protective factor for reducing residual lymph node metastases in study patients whose primary tumor regression was TRG1 (OR=0.147, 95%CI: 0.026-0.828, P=0.030). Conclusion: Compared with preoperative chemotherapy alone, a combination of preoperative immunotherapy and chemotherapy achieved greater reduction of residual lymph node metastases in the study patients who achieved TRG1 tumor regression in their primary lesions.

Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer.

To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparisonwith the ESGAR category and size of lymph node (LN). Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinicalfeatures was also established and evaluated. Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037). The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.