Tumor Characteristics Research Articles

Residual Nodal Burden After Neoadjuvant Chemotherapy in cN1 Breast Cancer Patients with Positive Nodes at Targeted Axillary Dissection.

Targeted axillary dissection (TAD) facilitates nodal staging in cN1 breast cancer after neoadjuvant chemotherapy (NAC). Completion axillary node dissection (cALND) remains the standard of care for TAD-positive patients. This study investigated factors associated with additional positive nodes at cALND (cALND+) and the impact on the residual cancer burden (RCB). Retrospective review of cN1 breast cancer patients treated with NAC and TAD was conducted from July 2013 to June 2023. The review defined cN1 status by ultrasound (US) and biopsy. Patient, tumor, and treatment characteristics were evaluated. Multivariate analysis was performed to identify factors associated with cALND+, and RCB was calculated. Of 902 patients who underwent TAD, 554 (61.4%) were TAD-positive. 457 underwent cALND, and 124 (27%) were cALND+ (average 4.1 additional +nodes). The cALND+ patients had larger primary tumors at diagnosis (4 vs 3.5 cm; p=0.04), more than three suspicious nodes on initial US (30% vs 13%; p≤0.0001), larger residual primary tumors on pathology (median, 3 vs 2.1 cm; p=0.0004), and more positive TAD nodes (median, 2 vs 1; p≤0.0001). In the multivariate analysis, the factors associated with cALND+ were more than three suspicious nodes on initial US (odds ratio [OR], 2.9; p≤0.0001), more positive TAD nodes (OR, 1.1; p≤0.0001), larger clipped node metastasis (OR, 1.1; p≤0.0001), and larger residual tumor on pathology (OR, 1.1; p=0.006). Of 65 cALND+ patients with RCB class I or II, 29 (45%) had an increase in RCB based on cALND. Of cN1 breast cancer patients treated with NAC who are TAD-positive, approximately 25% will have additional nodal disease on cALND. In these patients, positive cALND is associated with greater disease burden, which has potential implications for RCB status and prognosis.

Spatial resolution of the head and neck cancer tumor microenvironment to identify tumor and stromal features associated with therapy response.

Head and neck cancer (HNC) is the seventh most common cancer globally, resulting in 440 000 deaths per year. While there have been advancements in chemoradiotherapy and surgery, relapse occurs in more than half of HNCs, and these patients have a median survival of 10 months and a 2-year survival of < 20%. Only a subset of patients displays durable benefits from immunotherapies in metastatic and recurrent HNC, making it critical to understand the tumor microenvironment (TME) underpinning therapy responses in HNC. To recognize biological differences within the TME that may be predictive of immunotherapy response, we applied cutting-edge geospatial whole-transcriptome profiling (NanoString GeoMx Digital Spatial Profiler) and spatial proteomics profiling (Akoya PhenoCycler-Fusion) on a tumor microarray consisting of 25 cores from 12 patients that included 4 immunotherapy-unresponsive (8 cores) and 2 immunotherapy-responsive patients (5 cores), as well as 6 immunotherapy naïve patients (12 cores). Through high-plex, regional-based transcriptomic mapping of the tumor and TME, pathways involved with the complement system and hypoxia were identified to be differentially expressed in patients who went on to experience a poor immunotherapy response. Single-cell, targeted proteomic analysis found that immune cell infiltration of the cancer cell mass and interactions of CD8 Tcells with tumor and other immune cells were associated with positive immunotherapy response. The relative abundance of specific tumor phenotypes and their interactions with various immune cells was identified to be different between response groups. This study demonstrates how spatial transcriptomics and proteomics can resolve novel alterations in the TME of HNC that may contribute to therapy sensitivity and resistance.

Clinicopathological, molecular, and prognostic features of colorectal carcinomas with KRAS c.34G>T (p.G12C) mutation.

Evidence indicates that combinations of anti-EGFR antibodies and KRAS p.G12C (c.34G>T) inhibitors can be an effective treatment strategy for advanced colorectal cancer. We hypothesized that KRAS c.34G>T (p.G12C)-mutated colorectal carcinoma might be a distinct tumor subtype. We utilized a prospective cohort incident tumor biobank (including 1347 colorectal carcinomas) and detected KRAS c.34G>T (p.G12C) mutation in 43 cases (3.2%) and other KRAS mutations (in codon 12, 13, 61, or 146) in 467 cases (35%). The CpG island methylator phenotype (CIMP)-low prevalence was similarly higher in KRAS c.34G>T mutants (52%) and other KRAS mutants (49%) than in KRAS-wild-type tumors (31%). KRAS c.34G>T mutants showed higher CIMP-high prevalence (14%) and lower CIMP-negative prevalence (33%) compared with other KRAS mutants (6% and 45%, respectively; p = 0.0036). Similar to other KRAS mutants, KRAS c.34G>T-mutated tumors were associated with cecal location, non-microsatellite instability (MSI)-high status, BRAF wild type, and PIK3CA mutation when compared with KRAS-wild-type tumors. Compared with BRAF-mutated tumors, KRAS c.34G>T mutants showed more frequent LINE-1 hypomethylation, a biomarker for early-onset colorectal carcinoma. KRAS c.34G>T mutants were not associated with other features, including the tumor tissue abundance of Fusobacterium nucleatum (F. animalis), pks+ Escherichia coli, Bifidobacterium, or (enterotoxigenic) Bacteroides fragilis. Among 1122 BRAF-wild-type colorectal carcinomas, compared with KRAS-wild-type tumors, multivariable-adjusted colorectal cancer-specific mortality hazard ratios (95% confidence interval) were 1.82 (1.05-3.17) in KRAS c.34G>T (p.G12C)-mutated tumors (p = 0.035) and 1.57 (1.22-2.02) in other KRAS-mutated tumors (p = 0.0004). Our study provides novel evidence for clinical and tumor characteristics of KRAS c.34G>T (p.G12C)-mutated colorectal carcinoma.

Prospective selective embedding of radical prostatectomy specimens is not inferior to full embedding regarding established and new prognostic parameters.

The histopathological examination of radical prostatectomy specimens is essential for assessing critical tumor characteristics, including stage, grade, and margins, all of which impact patient prognosis. However, the extent of embedding the prostate has long been a subject of debate, with some advocating partial/selective embedding and others favoring complete embedding. This study establishes a standardized and time-efficient protocol for processing radical prostatectomy specimens with limited embedding while maintaining diagnostic accuracy. Two hundred twenty-six prostatectomy specimens were analyzed, and the results of a highly standardized selective embedding protocol, systematically embedding the apex, the base, the transition to the seminal vesicles, and selected horizontal sections, were compared with full embedding as the gold standard. Non-inferiority testing was conducted by one-sided binomial tests and Pearson-Clopper confidence intervals. Selective embedding provided consistent and accurate diagnostic information with up to 90-98% concordance in pT, margins, ISUP-grade groups, and presence of IDC-P and cribriform tumor growth. In summary, this study establishes an economical standardized protocol for selective embedding of radical prostatectomy specimens with only minimal loss of information.

Hexokinase 2 as an independent risk factor for worse patient survival in esophageal adenocarcinoma and as a potential therapeutic target protein: A retrospective, single‑center cohort study.

Cancer cells exhibit a distinct metabolic profile that features an upregulation of less efficient glycolysis accompanied by lactate production for energy generation, in contract to the characteristic metabolism of normal cells. Consequently, cancer research has focused on the enzymes that participate in these cancer metabolic pathways. Among them, hexokinase 2 (HK2) has an important position as the initial enzyme in the glycolytic pathway. Increased expression levels of HK2 have been correlated with an increased risk of poor patient outcomes and advanced tumor stages in a number of malignant tumors, such as gastric carcinoma. The present study aimed to investigate the specific role of HK2 in patients diagnosed with esophageal adenocarcinoma. A total of 643 patients with esophageal adenocarcinoma were included. Immunohistochemical staining and HK2 mRNA in situ probes were used to investigate the association of HK2 expression levels with clinical and molecular tumor characteristics. Patients who exhibited high HK2 expression levels demonstrated significantly reduced overall survival (OS) times compared with patients who exhibited low HK2 expression levels (29.6 vs. 39.9 months, respectively; P=0.027). Furthermore, high HK2 expression levels were demonstrated to be an independent risk factor for reduced patient survival (hazard ratio, 1.65; 95% CI, 1.09-2.50; P=0.018). Significantly reduced patient survival was also demonstrated in the subgroups of male patients, patients with primarily resected tumors, patients with HER2-negative tumors and patients with tumors exhibiting Y chromosome loss. Elevated expression of HK2 was identified as a risk factor for unfavorable patient survival in esophageal adenocarcinoma. This revelation suggests the potential for future diagnostic and therapeutic avenues tailored to this specific patient subset. Identifying patients with high HK2 expression may pinpoint a higher-risk cohort, paving the way for comprehensive prospective studies that could advocate for intensified monitoring and more aggressive therapeutic regimens. Furthermore, the targeted inhibition of HK2 could hold promise as a strategy to potentially enhance patient outcomes.

Assessing the long-term prognostic ability of the 70 gene expression signature MammaPrint in an Italian single-center prospective cohort study of early-stage intermediate-risk breast cancer patients

PurposeThe aim of this study was to assess the prognostic performance of the 70-gene signature, MammaPrint, in an Italian single-center prospective cohort of early-stage intermediate-risk breast cancer (BC) patients. MethodsA total of 195 eligible early BC cases were tested for genomic risk between 2006 – 2013. In this retrospective analysis, the association of genomic risk with distant metastasis-free survival (DMFS) and overall survival (OS) were assessed using Cox regression models, adjusting for clinical and pathological tumor characteristics. ResultsMammaPrint identified 118 (60.5%) patients with genomically Low Risk tumors and 77 (39.5%) patients with genomically High Risk tumors. Age, menopausal status, tumor size, receptor status, and nodal status were comparable between MammaPrint Risk categories. The median follow-up was 8.4 years for DMFS and 9.3 years for OS; 8-year follow-up was reported for both endpoints. The 8-year DMFS was 90.4% (95% CI 84.9 – 95.9) in patients with MammaPrint Low Risk tumors compared to 60.8% (95% CI 49.8 – 71.8) for patients with High Risk tumors. Patients with MammaPrint Low Risk tumors exhibited significantly superior 8-year OS (97.3%; 95% CI 94.4 – 100) compared with MammaPrint High Risk tumors (89.5%; 95% CI 82.6 – 96.4; p = 0.028). Multivariate analyses identified MammaPrint as significantly associated with 8-year DMFS and MammaPrint together with Progesterone Receptor positivity with 8-year OS. ConclusionThe prognostic performance of MammaPrint was demonstrated in early-stage clinically intermediate to high-risk BC patients. Moreover, patients with MammaPrint Low Risk tumors had good outcome regardless of treatment regimen, thus supporting personalized treatment choices.

Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

A new nano approach to prevent tumor growth in the local treatment of glioblastoma: Temozolomide and rutin-loaded hybrid layered composite nanofiber

Total resection of glioblastoma (GB) tumors is nearly impossible, and systemic administration of temozolomide (TMZ) is often inadequate. This study presents a hybrid layered composite nanofiber mesh (LHN) designed for localized treatment in GB tumor bed. The LHN, consisting of polyvinyl alcohol and core-shell polylactic acid layers, was loaded with TMZ and rutin. In vitro analysis revealed that LHN™Z and LHNrutin decelerated epithelial-mesenchymal transition and growth of stem-like cells, while the combination, LHN™Z+rutin, significantly reduced sphere size compared to untreated and LHN™Z-treated cells (P < 0.0001). In an orthotopic C6-induced GB rat model, LHN™Z+rutin therapy demonstrated a more pronounced tumor-reducing effect than LHN™Z alone. Tumor volume, assessed by magnetic resonance imaging, was significantly reduced in LHN™Z+rutin-treated rats compared to untreated controls. Structural changes in tumor mitochondria, reduced membrane potential, and decreased PARP expression indicated the activation of apoptotic pathways in tumor cells, which was further confirmed by a reduction in PHH3, indicating decreased mitotic activity of tumor cells. Additionally, the local application of LHNs in the GB model mitigated aggressive tumor features without causing local tissue inflammation or adverse systemic effects. This was evidenced by a decrease in the angiogenesis marker CD31, the absence of inflammation or necrosis in H&E staining of the cerebellum, increased production of IFN-γ, decreased levels of interleukin (IL)-4 in splenic T cells, and lower serum AST levels. Our findings collectively indicate that LHN™Z+rutin is a promising biocompatible model for the local treatment of GB.

HQNet: A hybrid quantum network for multi-class MRI brain classification via quantum computing

The complex and nonlinear nature of brain tumor morphology and texture pose significant challenges for representative feature extraction from magnetic resonance imaging (MRI) images, ultimately leading to inaccurate diagnoses of brain tumors. To tackle this problem, we propose a novel approach termed the hybrid quantum–classical convolutional network (HQNet) for detecting MRI brain tumors. Firstly, we use data augmentation techniques to deal with the issues of class imbalance and the sample scarcity within brain tumor MRI images. Secondly, we integrate channel attention and self-attention mechanisms to capture both channel and spatial dependencies from MRI images. This integration enables us to effectively fuse these dependencies and mitigate the discrepancies between them. However, the varying dependence of brain tumors at different scales can result in a decline in classification performance. To address this issue, we extract brain tumor features at various scales and combine them to alleviate this effect. Thirdly, a new parameterized quantum circuit is designed based on the principles of quantum computing to maximize the potential advantages of quantum computing and improve feature extraction. Finally, we compress the features and pass them through the fully connected layer to generate the final classification result. The effectiveness of our model is demonstrated through its superior performance when evaluated using three benchmark datasets.

A patient-derived HCC spheroid system to model the tumor microenvironment and treatment response

Background & AimsHepatocellular carcinoma (HCC) is the third leading and fastest rising cause of cancer-related death worldwide. The discovery and preclinical development of compounds targeting HCC is hampered by the absence of authentic tractable systems recapitulating the heterogeneity of patient HCC tumors and the tumor microenvironment (TME). MethodsWe established a novel and simple patient-derived multicellular tumorspheroid model based on clinical HCC tumor tissues, processed using enzymatic and mechanical dissociation. After quality controls, 22 HCC tissues and 17 HCC serum were selected for tumorspheroid generation and perturbation studies. Cells were grown in 3D in optimized medium in presence of patient serum. Characterization of the tumorspheroid cell populations was performed by flow cytometry, immunohistochemistry and functional assays. As a proof of concept, we treated patient-derived spheroids with FDA approved anti-HCC compounds. ResultsThe model was successfully established independently from cancer etiology and grade from 22 HCC tissues. We show that the use of HCC patient serum was essential for tumorspheroid generation, the TME function and to maintain cell viability. The tumorspheroids comprised the main cell compartments including epithelial cancer cells as well as all major cell populations of the TME (cancer-associated fibroblasts, macrophages and T cells, endothelial cells). Tumorspheroids reflect HCC heterogeneity, including variability in cell type proportions and TME, and mimic the original tumor features. Moreover, we observed differential responses to FDA HCC approved drugs between donors as observed in patients. ConclusionThis patient HCC serum-tumorspheroid model provides novel opportunities for drug discovery and development as well as mechanism of action studies including compounds targeting the TME. This model will likely contribute to improve the dismal outcome of patients with HCC. Impact and implicationsHepatocellular carcinoma (HCC) is a leading and fast rising cause of cancer-related death worldwide. Despite approval of novel therapies, the outcome of advanced HCC remains unsatisfactory. By developing a novel patient-derived tumorspheroid model recapitulating tumor heterogeneity and microenvironment, we provide new opportunities for HCC drug development and analysis of mechanism of action in authentic patient tissues. The application of the patient-derived tumorspheroids combined with other HCC models will likely contribute to drug development and to improve the dismal outcome of patients with HCC.

Pre‑operative mean platelet volume is associated with overall survival in patients with IDH‑wildtype glioblastoma undergoing maximal safe resection.

Glioblastoma (GBM) is the most common, fast-growing, and aggressive malignant primary CNS tumor, with a survival time of ~15 months despite the use of surgery and adjuvant treatments. In recent years, there has been a growing interest in exploring the potential contribution of hemostasis and platelet activation in GBM biology. The present study assessed the association between the pre-operative coagulation profile [as indicated by prothrombin time (PT) ratio and activated partial thromboplastin time (aPTT) ratio], overall platelets (PLT) count and the mean platelet volume (MPV) with tumoral characteristics and overall survival in patients with isocitrate dehydrogenase-wildtype (IDH-wt) GBM.

Brain Tumor Detection and Classification using YOLOv10 and AI Chatbot Using LLMs

The integration of advanced medical imaging techniques and artificial intelligence (AI) has greatly improved the early detection and diagnosis of brain tumors. This paper introduces a novel system for brain tumor detection and classification using the YOLOv10 (You Only Look Once) model, enhanced by an AI chatbot powered by Large Language Models (LLMs). Leveraging the real-time object detection capabilities of YOLOv10, the system accurately classifies brain tumors from MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor. This deep learning-based approach ensures swift and precise analysis of complex medical images. In addition, an AI chatbot is integrated to provide seamless interaction and information retrieval for both patients and healthcare professionals. Utilizing LLMs, the chatbot offers advanced conversational abilities, delivering detailed explanations about tumor types, potential treatments, and further medical advice based on the detected tumor characteristics. This dual-system approach aims to enhance diagnostic accuracy while providing real-time, accessible support, ultimately improving decision-making for medical practitioners and enhancing patient outcomes. The proposed system exemplifies the synergy between cutting-edge AI technologies and medical diagnostics, showcasing the potential to revolutionize patient care.

Circulating RKIP and pRKIP in Early-Stage Lung Cancer: Results from a Pilot Study

Background: Lung cancer (LC) is the leading cause of cancer-related deaths. Although low-dose computed tomography (LD-CT) reduces mortality, its clinical use is limited by cost, radiation, and false positives. Therefore, there is an urgent need for non-invasive and cost-effective biomarkers. The Raf Kinase Inhibitor Protein (RKIP) plays a crucial role in cancer development and progression and may also contribute to regulating the tumor–immune system axis. This protein has recently been described in biological fluids. Therefore, we conducted a pilot case–control study to assess RKIP and phosphorylated RKIP (pRKIP) levels in the urine and blood of LC patients. Methods: A novel enzyme linked immunosorbent assay (ELISA) assay was used to measure RKIP and pRKIP levels in urine and blood samples of two cohorts of LC patients and healthy controls (HSs). Furthermore, the biomarkers levels were correlated with tumor characteristics. Results: Serum, but not urine, levels of RKIP were significantly elevated in LC patients, distinguishing them from low- and high-risk healthy subjects with 93% and 74% accuracy, respectively. The RKIP/pRKIP ratio (RpR score) showed an accuracy of 90% and 79% in distinguishing LC patients from HS and HR-HS, respectively. Additionally, the RpR score correlated better with dimension, stage, and lymph node involvement in the tumor group. Conclusions: The serum RKIP and pRKIP profile may be a promising novel biomarker for early-stage LC.

Demographic and Clinical Characteristics of Malignant Solitary Fibrous Tumors: A SEER Database Analysis

Background/Objectives: Solitary fibrous tumors (SFTs) represent a rare mesenchymal malignancy that can occur anywhere in the body. Due to the low prevalence of the disease, there is a lack of contemporary data regarding patient demographics and cancer-control outcomes. Methods: Within the SEER database (2000–2019), we identified 1134 patients diagnosed with malignant SFTs. The distributions of patient demographics and tumor characteristics were tabulated. Cumulative incidence plots and competing risks analyses were used to estimate cancer-specific mortality (CSM) after adjustment for other-cause mortality. Results: Of 1134 SFT patients, 87% underwent surgical resection. Most of the tumors were in the chest (28%), central nervous system (22%), head and neck (11%), pelvis (11%), extremities (10%), abdomen (10%) and retroperitoneum (6%), in that order. Stage was distributed as follows: localized (42%) vs. locally advanced (35%) vs. metastatic (13%). In multivariable competing risks models, independent predictors of higher CSM were stage (locally advanced HR: 1.6; metastatic HR: 2.9), non-surgical management (HR: 3.6) and tumor size (9–15.9 cm HR: 1.6; ≥16 cm HR: 1.9). Conclusions: We validated the importance of stage and surgical resection as independent predictors of CSM in malignant SFTs. Moreover, we provide novel observations regarding the independent importance of tumor size, regardless of the site of origin, stage and/or surgical resection status.

Digital mapping of resected cancer specimens: The visual pathology report

BackgroundThe current standard-of-care pathology report relies only on lengthy written text descriptions without a visual representation of the resected cancer specimen. This study demonstrates the feasibility of incorporating virtual, three-dimensional (3D) visual pathology reports to improve communication of final pathology reporting. Materials and methodsSurgical specimens are 3D scanned and virtually mapped alongside the pathology team to replicate grossing. The 3D specimen maps are incorporated into a hybrid visual pathology report which displays the resected specimen and sampled margins alongside gross measurements, tumor characteristics, and microscopic diagnoses. ResultsVisual pathology reports were created for 10 head and neck cancer cases. Each report concisely communicated information from the final pathology report in a single page and contained significantly fewer words (293.4 words) than standard written pathology reports (850.1 words, p < 0.01). ConclusionsWe establish the feasibility of a novel visual pathology report that includes an annotated visual model of the resected cancer specimen in place of lengthy written text of standard of care head and neck cancer pathology reports.

The prevalence of ER-low-positive breast cancer and its relation to tumor characteristics in Syria

Abstract Background: Breast cancer is a heterogeneous disease with varying clinical behaviors and responses to endocrine therapy. In particular, estrogen receptor (ER)-positive breast cancer can present with various subtypes, and the effectiveness of anti-hormone therapy on ER-low-positive tumors is unclear. This study aimed to evaluate and characterize ER-positive subtypes in patients admitted to the Surgical Oncology Clinic in the National Hospital of Jableh in Syria between 2020 and 2022. Patients and Methods: This study included 120 patients diagnosed with breast cancer; the data from each patient’s report were collected to classify them according to grade, stage, LNR, tumor size, and nodal stage. Results: The prevalence of ER-low-positive subtype was 23.33%. There was a significant difference between ER-negative and ER-positive subtypes concerning age, grade, stage, and LNR. Additionally, we confirmed a variation between ER-low-positive and ER-high-positive tumors, which may explain the difference in therapeutic response in ER-positive tumor patients taking anti-ER drugs. Conclusion: Further research is necessary to study the association between therapeutic response and ER-staining intensity breast cancer subtypes.

Prognostic role of pre-diagnostic circulating inflammatory biomarkers in breast cancer survival: evidence from the EPIC cohort study

BackgroundInflammation influences tumour progression and cancer prognosis, but its role preceding breast cancer (BC) and its prognostic implications remain inconclusive.MethodsWe studied pre-diagnostic plasma inflammatory biomarkers in 1538 women with BC from the EPIC study. Cox proportional hazards models assessed their relationship with all-cause and BC-specific mortality, adjusting for tumour characteristics and lifestyle factors.ResultsOver a 7-year follow-up after diagnosis, 229 women died, 163 from BC. Elevated IL-6 levels were associated with increased all-cause mortality risk (HR1-SD 1.25, 95% CI 1.07–1.47). Among postmenopausal, IL-6 was associated with higher all-cause (HR1-SD 1.41, 95% CI 1.18–1.69) and BC-specific mortality (HR1-SD 1.31, 95% CI 1.03–1.66), (PHeterogeneity (pre/postmenopausal) < 0.05 for both), while IL-10 and TNFα were associated with all-cause mortality only (HR1-SD 1.19, 95% CI 1.02–1.40 and HR1-SD 1.28, 95% CI 1.06–1.56). Among ER+PR+, IL-10 was associated with all-cause and BC-specific mortality (HR1-SD 1.35, 95% CI 1.10–1.65 and HR1-SD 1.42 95% CI 1.08–1.86), while TNF-α was associated with all-cause mortality in HER2- (HR1-SD 1.31, 95% CI 1.07–1.61). An inflammatory score predicted higher all-cause mortality, especially in postmenopausal women (HR1-SD 1.30, 95% CI 1.07–1.58).ConclusionsHigher pre-diagnosis IL-6 levels suggest poorer long-term survival among BC survivors. In postmenopausal survivors, elevated IL-6, IL-10, and TNFα and inflammatory scores seem to predict all-cause mortality.

Dual phase hierarchical brain tumour detection and segmentation using unet based skip guidance residual convolutional transformer

Brain tumour segmentation and classification is an important approach to understanding tumour characteristics effectively. Accurate lesion segmentation requires many image modalities with varied contrasts. As a result, manual segmentation may not be suitable for large-scale research. The research has shown interest in developing various learning algorithms to detect and segment brain tumour (BT) images accurately based on the set of experimental images. Some of the issues noticed in the existing models, such as high computational complexity, high processing time, and less accuracy, were identified. In order to overcome these existing issues, a novel Dual-phase channel attentional Residual convolutional transformer Unet model (DCARCTUnet) is introduced for segmenting brain tumours. Initially, the input images are passed into the pre-processing stage to remove noise and blur. The Efficient Net model identified the pre-processed image as either normal or tumour. The proposed segmentation model can be integrated into three stages, namely dual phase channel attention module (DCAM), residual convolutional transformer Unet model and Pyramid skip guidance module (PSM). The proposed method uses the BRATS 2020–2021 dataset to analyze the results accurately. The results showed that the proposed model has a dice coefficient value of 0.99 and a Jaccard coefficient value of 0.98. Furthermore, performance analysis is carried out to demonstrate the effectiveness of the proposed model.

Feasibility, safety and effectiveness of robot-assisted radical prostatectomy with a new robotic surgical system: a prospective, controlled, randomized clinical trial

BackgroundRobot-assisted radical prostatectomy (RARP) gains increasing popularity in the surgical management of prostate cancer (PCa) but is challenged by its prohibitive expense. A domestic robotic system has been developed to address this issue, but data comparing the self-developed robot with the widely used robot is lacking. We performed a randomized clinical trial to compare KD-SR-01® and DaVinci® robots in terms of perioperative, short-term oncological and functional outcomes in RARP.Materials and methodsWe prospectively enrolled patients with clinically localized PCa. Patients were randomized to undergo either KD-SR-01®-RARP (K-RARP) or DaVinci®-RARP (D-RARP) by the same surgical team. The baseline, perioperative, short-term oncologic and urinary functional data were collected and compared.ResultsWe enrolled 39 patients, including 20 patients undergoing K-RARP and 19 undergoing D-RARP. Demographic and tumor characteristics were comparable between groups. All surgeries were performed successfully with no conversion to open. The operative time was similar (P = 0.095) and K-RARP offered less volume of intraoperative bleeding (P < 0.001). Four patients in the K-RARP group and three in the D-RARP group developed postoperative complications (P = 0.732). Patients undergoing K-RARP had less volume of drainage (P = 0.022). Positive surgical margins were observed in three patients undergoing K-RARP and five undergoing D-RARP (P = 0.451). During the follow up, one patient receiving K-RARP group and two receiving D-RARP group had measurable prostate specific antigen (P = 0.605). Urine leakage, urinary control and pad usage were comparable between groups at six weeks post-surgery.ConclusionsThe two surgical robots yielded similar results in feasibility, safety and short-term oncologic and functional efficacy for RARP.Trial registrationThe trial has been registered at www.chictr.org.cn with a registration number of ChiCTR2200057000 on 25th February 2022.

Diagnostic Utility of a 90-Gene Expression Assay (Canhelp-Origin) for Patients with Metastatic Cancer with an Unclear or Unknown Diagnosis.

Metastatic cancers with unclear or unknown origins pose significant challenges in diagnosis and management, frequently leading to suboptimal outcomes. Studies have demonstrated that a 90-gene expression assay is effective in predicting the primary origin and guiding the site-specific therapy to improve prognosis. This study aimed to evaluate the clinical effectiveness of a 90-gene expression assay in patients with unclear or unknown diagnoses. The study encompassed patients for whom a 90-gene expression assay was requested as part of standard care. Data on patient demographics, tumor characteristics, and clinical history were collected. The assay's performance was evaluated by comparing its predicted tumor type with the final histopathological diagnosis. Among 303 cases analyzed, a 90-gene expression assay successfully identified a molecular-based tumor type for 295 (97.4%) patients. Comparison with histopathological diagnosis revealed an overall agreement of 88.5% (170/192). In patients with a single suspected primary site (n = 140), the assay confirmed the suspected diagnosis in 90.7% of cases. For those with a differential diagnosis (n = 52), the assay narrowed down the possibilities in 82.7% of cases. Moreover, in cases where the histopathology report indicated cancer of unknown primary (n = 103), the assay offered a molecular tumor type prediction with potential clinical significance. This study demonstrates the significant impact of a 90-gene expression assay on diagnosis and potential treatment selection for difficult-to-diagnose patients, highlighting its clinical value as a standardized molecular approach to streamline further diagnostic testing for patients with metastatic cancer of unclear or unknown origin. Further prospective study is required to assess whether employing molecular diagnostic classifiers enhances clinical outcomes in these patients.