Annals of Surgical Oncology最新文献

Background: Although lymph node involvement (LNI) is a critical prognostic factor guiding adjuvant therapy, randomized trials have failed to show a survival advantage of extended versus standard pelvic lymph node dissection (PLND) but have demonstrated increased morbidity. Refining PLND templates according to tumor characteristics, such as laterality, may improve the risk-benefit balance. This study aimed to clarify the relationship between bladder tumor location and the anatomical distribution of LNI in bladder cancer.

Patients and methods: We retrospectively reviewed 102 patients who underwent radical cystectomy with bilateral extended PLND at Kyushu University Hospital between 2013 and 2024. Tumor laterality was classified as unilateral or bilateral. LNI sites were categorized as ipsilateral versus contralateral and by level (I: obturator, internal/external iliac; II: common iliac, presacral).

Results: Overall, 17.6% of patients had LNI. Bilateral tumors were associated with higher nodal metastasis than were unilateral tumors (24.2% vs. 14.5%). In unilateral tumors, contralateral LNI without ipsilateral involvement occurred in only 1.5% of cases. Level II metastasis was uncommon (5.9%), and skip metastasis to level II nodes without level I involvement was rare (1.0%).

Conclusions: Tumor laterality is a strong predictor of nodal distribution. The rarity of contralateral or skip metastasis warrants prospective studies to validate tailored, tumor location-based PLND strategies.

Background: In patients with hormone receptor-positive (HR⁺), HER2-negative breast cancer, there is a lack of data available to define optimal axillary management strategies after neoadjuvant endocrine therapy (NET).

Patients and methods: We performed a retrospective review of patients with clinical stage I-III, HR⁺ breast cancer who received > 90 days of NET at a single comprehensive cancer center between 2004 and 2024.

Results: A total of 230 patients were included; 120 (52.2%) were clinically node-negative (cN0), while 110 (47.8%) were clinically node-positive (cN+). In the cN+ group, 7.3% (8/110) had a nodal pathologic complete response (pCR). In total, 76.4% (84/110) in the cN+ group underwent axillary lymph node dissection (ALND) as the initial axillary procedure, and 90.9% (100/110) underwent ALND overall. In total, 98.3% (118/120) with cN0 disease underwent sentinel lymph node dissection (SLND), and 28 (23.7%) had positive nodes. A total of 64.3% (18/28) of cN0 patients with a positive SLN underwent ALND. There were four local-regional recurrences (LRR) in the entire cohort, yielding a 5-year LRR rate of 2.2%, and it did not differ between cN0 and cN+ (2.0% versus 2.2%, p >0.05). The distant recurrence rate was higher in cN+ compared with the cN0 group (15.0% versus 6.9%, p = 0.03). The 5-year overall survival (OS) was 79.5% for the entire cohort and was higher for cN0 compared with cN+ patients (88.4% versus 69.7%, p = 0.004).

Conclusions: The 5-year LRR rate was extremely low for the entire cohort, suggesting that NET is an oncologically safe strategy for patients with HR⁺ disease even in the setting of large tumors and positive lymph nodes.

Background: Accurate pathological classification of lung cancer is essential for informing treatment strategies. However, invasive biopsy procedures are not feasible for high-risk patients or those with inaccessible lesions. This study aimed to develop a machine learning model utilizing routine clinical and laboratory data for classification of non-invasive lung cancer.

Methods: Data from patients admitted to Sichuan Provincial Cancer Hospital were retrospectively analyzed. Key features were determined using LASSO and Boruta algorithms. Four machine learning models, including logistic regression, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and random forest (RandomForest), were trained and optimized through five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, and F1 score. An online calculator was developed using R Shiny for clinical deployment.

Results: A total of 1122 patients with lung cancer were included and randomly assigned to the training and test sets. In the training set, 16 features were incorporated into the models. The RandomForest model demonstrated superior performance compared with the other models, achieving an AUC of 0.999, an accuracy of 0.984, and an F1 score of 1.000. Notably, sex and tumor markers were identified as significant predictors. In the test set, the RandomForest model attained a micro-averaged AUC of 0.969 and macro-averaged AUC of 0.940. Sensitivity and specificity varied from 0.667 to 0.995 across subtypes. A web-based tool was implemented to facilitate real-time clinical application ( https://nkuwangkai.shinyapps.io/lung-cancer-v1/ ).

Conclusion: This study presented a robust, non-invasive machine learning model for lung cancer subtype classification, addressing critical gaps in clinical practice for biopsy-ineligible patients. A web-based calculator was developed to facilitate clinical application. Nonetheless, future multicenter validation is warranted to expand the generalizability of this model and promote adoption in diverse healthcare settings.

Objectives: Our objective was to investigate TRIM3 expression and its role in the immune microenvironment of non-small-cell lung cancer (NSCLC).

Materials and methods: TRIM3 levels in NSCLC tissues and cells were detected by reverse transcriptase quantitative polymerase chain reaction. Cell viability and proliferation of lung cancer cells were evaluated by CCK-8 and EdU methods under the interference of TRIM3 expression. Meanwhile, CD8+ T cells were co-cultured with lung cancer cells, and the cytotoxicity against lung cancer cells was determined with a lactate dehydrogenase cytotoxicity detection kit. The role of TRIM3 on regulating tumor growth in vivo was also investigated in subcutaneous tumor xenograft models. The protein interaction between TRIM3 and programmed cell death-ligand 1 (PD-L1) was also studied according to immunoprecipitation followed by Western blotting assay.

Results: The messenger RNA levels of TRIM3 were significantly lower in lung cancers than in adjacent normal lung tissues according to the GEPIA analysis. The messenger RNA and protein levels of TRIM3 were all minimally expressed in collected NSCLC tissues and lung cancer cells. Cell viability of both A549 and H1299 cells with high expression of TRIM3 was significantly inhibited according to the results of the CCK-8 assay and EdU methods. TRIM3 over-expression promotes CD8+ T-cell cytotoxicity against lung cancer cells and inhibits tumor growth. Overexpression of TRIM3 can upregulate the ubiquitination level of PD-L1 and reduce the stability of PD-L1.

Conclusions: This study reveals that TRIM3 functions as a tumor suppressor that can impede the tumorigenesis of NSCLC by degrading PD-L1, suggesting a novel therapeutic strategy against NSCLC.

Background: The propensity of desmoplastic melanoma (DM) to spread to regional lymph nodes remains disputed, creating uncertainty regarding the role of sentinel lymph node biopsy (SLNB). This is further complicated by challenges in distinguishing pure from mixed histologic subtypes. We evaluated pooled SLN positivity rates for both subtypes to clarify the utility of SLNB in the workup of patients with DM.

Methods: Two databases (PubMed and Ovid MEDLINE) were searched through July 2025. We included studies in which at least a subset of patients with DM underwent SLNB and SLNB positivity rates could be ascertained; large registry-based studies were excluded. Our primary outcome was the pooled sentinel lymph node positivity rate for patients with DM. We performed a subgroup analysis to determine pooled SLN positivity by histologic subtype.

Results: We included 18 studies with 1671 patients with DM. A random-effects meta-analysis demonstrated a pooled SLN positivity rate of 9% (95% CI, 7-12%). No significant association was found between positivity and Breslow depth (β = 0.058, p = 0.749) or ulceration (β = 0.763, p = 0.677). Ten studies reported subtype-specific rates. The pooled positivity rate was 6% (95% CI, 4-8%) for pure DM and 15% (95% CI, 10-20%) for mixed DM.

Conclusions: Patients with mixed DM may derive greater prognostic and diagnostic benefit from SLNB, whereas SLNB in pure DM may be considered selectively, particularly in the setting of histopathologic uncertainty or other high-risk features.