Search

Background: The purpose of this work was to investigate the prognostic significance of Ki67 in acral melanoma (AM). Patients and Methods: Ki67 values in primary lesions (pKi67) of 481 patients with primary non-metastatic AM (primary cohort) from three tertiary hospitals and in recurrent lesions (rKi67) of 97 patients (recurrent cohort) were recorded. The associations of p/rKi67 with clinicopathological features and prognosis were analyzed. Results: In the primary cohort, high pKi67 group tended to have more ulceration, pT4, lymph node metastasis (LNM), nodal macrometastases, and recurrence (all P < 0.05). Logistic regression analysis revealed that pKi67 was significantly associated with pT4 and LNM (P = 0.004 and 0.027, respectively). Furthermore, both 5-year overall survival (OS) and recurrence-free survival (RFS) rates in high pKi67 group were significantly worse than those in moderate and low pKi67 groups (OS 47.8% versus 55.7 versus 76.8%, P = 0.002; RFS: 27.1 versus 42.8 versus 61.8%, P < 0.001). Similarly, in the recurrent cohort, the 5-year survival after recurrence (SAR) rates in high rKi67 group was significantly worse than those in moderate and low rKi67 groups (31.7 versus 47.4 versus 75%; P = 0.026). Stratified analysis also indicated a significant survival difference among pKi67 groups within various subgroups. Most importantly, multivariate Cox analysis demonstrated that pKi67 could be independently associated with OS and RFS, as well as rKi67 for SAR (all P < 0.05). Conclusions: A high Ki67 value was significantly associated with adverse pathological and prognostic features in both primary and recurrent AM cohorts. Ki67 should be routinely evaluated to guide risk stratification and prognostic prediction. [ABSTRACT FROM AUTHOR]

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions. Correct diagnosis of primary central nervous system lymphoma is key in determining treatment, however, this depends on pathology analysis. Here, the authors develop a deep learning method to diagnose primary nervous system lymphoma from stained whole-slide images. [ABSTRACT FROM AUTHOR]

Objectives: The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery.Methods: A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method.Results: In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy.Conclusion: The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients.Key Points: • In the primary cohort, patients with FEP- and NC-type metastases had shorter disease-free survival (DFS) and a higher intrahepatic recurrence rate than patients without such metastases in the liver. • In the primary cohort, there were no significant differences in DFS or intrahepatic recurrence rate between patients with rough- and smooth-type metastases and those without such metastases in the liver. • High-risk patients had shorter DFS and a higher intrahepatic recurrence rate than low-risk patients in primary and external validation cohorts. [ABSTRACT FROM AUTHOR]

Background: CMTM6 is a novel key regulator of PD-L1. High expression of both CMTM6 and PD-L1 may predict the benefit of PD-1 axis blockade in lung cancer. We aimed to investigate the expression pattern of CMTM6 between mismatch repair-defective (dMMR) and mismatch repair-proficient (pMMR) colorectal cancer (CRC) tissues and assess its correlation with the response to PD-1/PD-L1 pathway blockade. Methods: Immunohistochemistry (IHC) was used to analyze CMTM6 and PD-L1 expression and immune cell density in dMMR/pMMR CRC. Quantitative multiplex immunofluorescence (IF) was performed to detect CMTM6, PD-L1, CD4, CD8, CD68 and CD163 expression in CRC patients treated with PD-1/PD-L1 inhibitors. Result: IHC analysis showed that CMTM6 and PD-L1 were both expressed in tumor cells (TCs) and invasion front immune cells (ICs). CMTM6 and PD-L1 expression and CD4+, CD8+, CD68+ or CD163+ cell density were significantly higher in dMMR CRC patients than in pMMR CRC patients. CMTM6 expression was positively correlated with PD-L1 expression and CD163+ M2 macrophage density in dMMR CRC. IF analysis showed that the coexpression rate of CMTM6/PD-L1 and the expression rate of CMTM6 in CD8+ T cells and CD163+ M2 macrophages were significantly increased in the group that exhibited clinical benefit. CMTM6 expression in M2 macrophages was identified as the best biomarker for predicting the responsiveness to PD-1/PD-L1 inhibitors. Conclusions: CMTM6 expression in M2 macrophages may predict the PD-1/PD-L1 inhibitor response rate in CRC patients more accurately than dMMR/microsatellite instability-high (MSI-H) status. It can also identify pMMR CRC patients who could benefit from PD-1/PD-L1 inhibitors. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic potential of intravoxel incoherent motion (IVIM) DWI for differentiating metastatic and non-metastatic lymph node stations (LNS) in pancreatic ductal adenocarcinoma (PDAC).Methods: 59 LNS histologically diagnosed following surgical resection from 15 patients were included. IVIM DWI with 12 b values was added to the standard MRI protocol. Evaluation of parameters was performed pre-operatively and included the apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f). Diagnostic performance of ADC, D, D* and f for differentiating between metastatic and non-metastatic LNS was evaluated using ROC analysis.Results: Metastatic LNS had significantly lower D, D*, f and ADC values than the non-metastatic LNS (p< 0.01). The best diagnostic performance was found in D, with an area under the ROC curve of 0.979, while the area under the ROC curve values of D*, f and ADC were 0.867, 0.855 and 0.940, respectively. The optimal cut-off values for distinguishing metastatic and non-metastatic lymph nodes were D = 1.180 × 10-3 mm2/s; D* = 14.750 × 10-3 mm2/s, f = 20.65 %, and ADC = 1.390 × 10-3 mm2/s.Conclusion: IVIM DWI is useful for differentiating between metastatic and non-metastatic LNS in PDAC.Key Points: • IVIM DWI is feasible for diagnosing LN metastasis in PDAC. • Metastatic LNS has lower D, D*, f, ADC values than non-metastatic LNS. • D-value from IVIM model has best diagnostic performance, followed by ADC value. • D* has the lowest AUC value. [ABSTRACT FROM AUTHOR]