Diagnosis Of Cancer Research Articles

Association of Intellectual and Developmental Disabilities With Worse Outcomes After Surgical Treatment of Cancer.

Patients with intellectual and developmental disabilities (IDD) face unique challenges resulting in disparities in their health care. We sought to define the effect that IDD had on achievement of a "textbook outcome" (TO) following a cancer operation among a nationally representative cohort of patients. Data on patients who underwent surgery for a malignant indication, including lung, breast, liver, biliary tract, pancreas, and colorectal, between 2014 and 2020 were extracted from the 100% Medicare Standard Analytical Files database. The association of IDD with TO (defined as the absence of postoperative complications, extended length of stay, 90-day readmission, and 90-day mortality), expenditures, and discharge status was assessed using multivariable logistic regression. Among 500,472 Medicare beneficiaries, 4,326 (0.9%) with IDD had a cancer diagnosis (breast, n=481; lung, n=419; hepatobiliary, n=194; pancreas, n=145; colorectal, n=3,087). Although overall incidence of TO was 50.5%, patients with IDD were less likely to achieve a TO than those without (37.1% vs 50.6%, respectively; odds ratio [OR], 0.50; 95% CI, 0.46-0.53; P<.001). On multivariable regression, patients with IDD had higher odds of a postoperative complication (OR, 1.53; 95% CI, 1.43-1.64), extended length of stay (OR, 2.06; 95% CI, 1.93-2.21), 90-day readmission (OR, 1.15; 95% CI, 1.07-1.24), 90-day mortality (OR, 1.90; 95% CI, 1.70-2.13), and discharge to a skilled nursing facility (OR, 4.28; 95% CI, 3.97-4.62) (all P<.001). Patients with IDD had a much lower chance of a postoperative TO, as well as discharge to a nonhome setting. The data highlight the need to improve the care of patients with IDD to assure equitable oncologic surgical care.

Comprehensive cataloging of miR-363 as a therapeutic &amp; non-invasive biomarker of prostate cancer

Background &amp; objectives Overcoming the challenge of early diagnosis of prostate cancer (PCa) by exploring molecular biomarkers is urgently needed. With this objective, this study was designed to explore the biomarker and therapeutic potential of miRNA (miR)-363-3p in PCa pathogenesis. Methods Total participants (n=188) were enrolled, and blood and tissue samples were collected from individuals categorized into the control group (n=55), benign prostate hyperplasia (BPH) group (n=60), PCa group (n=48), and castration-resistant PCa (CRPC) group (n=25). MiR expression profiling was carried out using quantitative polymerase chain reaction (qPCR), and biomarker analysis was conducted employing receiver operating characteristics (ROC) curve. The miR-363 target genes were predicted by in silico tools like Target Scan and starBasev 2.0 and its expression was validated by qPCR and association among them was established by using the STRING database. Results The results showed that the tumour-suppressive nature of miR-363-3p in both PCa tissues and serum were significantly higher than the control with a greater area under curve (AUC) was 0.969 (sensitivity: 85%; specificity 100%) and 0.988 (sensitivity: 97.5%; specificity: 87.5%), respectively. The targetome analysis of miR-363-3p revealed five target genes-NRAS, E2F3, PTEN, MDM2, and CCNE2 which were strongly associated with cell division and proliferation. The expression analysis of the target genes showed a significant tumour-suppression of PTEN gene and significant upregulation of oncogenic genes such as NRAS, E2F3, MDM2, and CCNE2. Interpretation &amp; conclusions Collectively, the findings of this study suggest that miR-363-3p may be a potential biomarker in differentiating individuals with PCa and CRPC from healthy controls. The miR-363-3p triggers various oncogenic genes (MDM2, NRAS, E2F3, CCNE2) and tumour suppressor genes (PTEN) that are actively involved in PCa progression and development.

Reconstruction error based implicit regularization method and its engineering application to lung cancer diagnosis

The automatic diagnosis of lung cancer via artificial intelligence faces two hotspot issues: (1) insufficient data and (2) excessive redundant information, which make it difficult for convolutional neural networks (CNNs) to learn discriminative information of lung cancer. In this paper, we present the reconstruction error based implicit regularization method (REbIRM) that regularizes CNNs at the loss layer. During each training iteration, the reconstruction errors introduced by the two-stage discriminative auto-encoder are used to sharpen the generalization ability of deep CNNs by improving the decision boundary. In the application process, the trained deep CNN is used for completing computed tomography (CT) diagnostics. The main clinical benefit of our approach is that it is domain independent, requiring no specialized knowledge, and can therefore be applied to different types of datasets. To the best of our knowledge, this is the first attempt to implicitly regularize CNNs based on the reconstruction errors. Finally, experimental results on three CT image classification datasets show that REbIRM can achieve impressive results and that, in conjunction with Dropout, it obtains the state-of-the-art performance. REbIRM is also robust to the selection of hyper-parameters and only has the sublinear influence on the convergence of deep CNNs. Besides, empirical and theoretical evidence are provided to indicate that REbIRM prefers to converges in a constrained parameter space with flatter minima, which explains why it can generalize to new data. Finally, the nature of REbIRM is further explored through visualization techniques to analyze how it works in training deep CNNs.

Human epidermal growth factor receptor 2(Her2)-targeted pH-responsive MR/NIRF bimodal imaging-mediated nano-delivery system for the diagnosis and treatment of undifferentiated thyroid cancer.

Undifferentiated thyroid cancer (ATC) is highly malignant and does not respond well to sorafenib (SRF) treatment owing to the lack of specificity of SRF targeting. Drug delivery nanosystems can improve the efficiencies of drug in treating various cancer types. However, many conventional drug delivery nanosystems lack targeting and exhibit unresponsive drug release. Therefore, we developed a pH-responsive nano-targeted drug delivery systems using human serum albumin (HSA) as a carrier to generate manganese dioxide (MnO2)@HSA nanoparticles (NPs), then encapsulated SRF and the fluorescent dye indocyanine green (ICG) and finally modifyed the targeting antibody pertuzumab in the outer layer of the nano complexes, resulting in SRF/ICG/MnO2@HSA-pertuzumab (HISMP) NPs. This system targets human epidermal growth factor receptor 2 on the cell membrane surface of thyroid cancer cells and is designed to accumulate at tumor sites. Then, pH-responsive release of divalent manganese ions, ICG, and SRF enables magnetic resonance/fluorescence (MR/NIRF) dual-modality imaging and precise drug delivery for diagnostic and therapeutic integration. Various characterization analyses including transmission electron microscopy, Fourier infrared spectroscopy, and particle size analysis confirm that we successfully synthesized HISMP NPs with a diameter of 150.709nm. The results of CCK8 cytotoxicity and apoptosis assays show that HISMP NPs exhibited high cytotoxicity and induce apoptosis in thyroid cancer cells. In vivo MR/NIRF imaging experiments confirmed that the HISMP NPs specifically aggregated at tumor sites and have good in vivo MR/NIRF imaging ability and effective anti-tumor activity. The nano-delivery system is expected to provide a theoretical foundation for the efficient ATC diagnosis and therapy.

Alternative splicing in the genome of HPV and its regulation.

Persistent infection with high-risk human papillomavirus (HR-HPV) is the main cause of cervical cancer. These chronic infections are characterized by high expression of the HPV E6 and E7 oncogenes and the absence of the L1 and L2 capsid proteins. The regulation of HPV gene expression plays a crucial role in both the viral life cycle and rare oncogenic events. Alternative splicing of HPV mRNA is a key mechanism in post-transcriptional regulation. Through alternative splicing, HPV mRNA is diversified into various splice isoforms with distinct coding potentials, encoding multiple proteins and influencing the expression of HPV genes. The spliced mRNAs derived from a donor splicing site within the E6 ORF and one of the different acceptor sites located in the early mRNA contain E6 truncated mRNAs, named E6*. E6* is one of the extensively studied splicing isoforms. However, the role of E6* proteins in cancer progression remains controversial. Here, we reviewed and compared the alternative splicing events occurring in the genomes of HR-HPV and LR-HPV. Recently, new HPV alternative splicing regulatory proteins have been continuously discovered, and we have updated the regulation of HPV alternative splicing. In addition, we summarized the functions of known splice isoforms from three aspects: anti-tumorigenic, tumorigenic, and other cancer-related functions, including not only E6*, but also E6^E7, E8^E2, and so on. Comprehending their contributions to cancer development enhances insights into the carcinogenic mechanisms of HPV and explores the potential utility of alternative splicing in the diagnosis and treatment of cervical cancer.

Source free domain adaptation for kidney and tumor image segmentation with wavelet style mining

Kidney cancer is a serious malignant disease, and early diagnosis along with precise segmentation are crucial for effective treatment. However, due to the scarcity of labelled medical image data, the development of the intelligent diagnosis of kidney cancer is restricted. To address this challenge, we propose a novel unsupervised domain adaptation (UDA) framework specifically designed for kidney and tumor CT image segmentation. Our framework consists of a generation phase and an adaptation phase. In the generation phase, we employ a wavelet-based style mining generator to create class-specific source-like images, facilitating domain alignment. In the adaptation phase, we introduce contrastive domain extraction and compact-aware domain consistency modules, enhancing feature-level and output-level adaptability through data augmentation techniques. Experimental results demonstrate that our method performs better in kidney and tumor segmentation tasks, exhibiting higher accuracy and generalization capability than state-of-the-art domain adaptation methods. This indicates that our approach has significant advantages in medical image segmentation for kidneys and tumors.

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AimDue to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease. MethodsFour Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach. ResultsThese three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity. ConclusionPredictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

A Polyvinyl Alcohol (PVA)-Based Phantom for Prostate Cancer Detection Using Multiparametric Ultrasound: A Validation Study

Multiparametric ultrasound (mpUS) enhances prostate cancer (PCa) diagnosis by using multiple imaging modalities. Tissue-mimicking materials (TMM) phantoms, favoured over animal models for ethical and consistency reasons, were created using polyvinyl alcohol (PVA) with varying molecular weights (Mw). Methods: Four PVA samples, varying in Mw with constant concertation, were mixed with glycerol, silicon carbide (SiC), and aluminium oxide (Al2O3). Phantoms with varying depth and inclusion sizes were created and tested using shear-wave elastography (SWE). An mpUS phantom was developed to mimic prostate tissue, including isoechoic and hypoechoic inclusions and vessels. The phantom was scanned using supersonic ultrasound, strain elastography, and Doppler ultrasound. Validation was performed using radical prostatectomy data and shear-wave elastography. Results: The acoustic properties varied with enhancers like glycerol and Al2O3. Low Mw PVA samples had a speed of sound ranging from 1547.50 ± 2 to 1553.70 ± 2.2 m/s and attenuation of 0.61 ± 0.062 to 0.63 ± 0.05 dB/cm/MHz. High Mw PVA samples ranged from 1555 ± 2.82 to 1566 ± 4.5 m/s and 0.71 ± 0.02 to 0.73 ± 0.046 dB/cm/MHz. Young’s modulus ranged from 11 ± 2 to 82.3 ± 0.5 kPa across 1 to 10 freeze-thaw cycles. Inclusion size, depth, and interaction statistically affect the SWE measurements with p-value = 0.056327, p-value = 8.0039 × 10−8, and p-value = 0.057089, respectively. SWE showed isoechoic inclusions, prostate tissue, and surrounding tissue only. The Doppler velocity was measured in three different inner diameters. Conclusion: PVA mixed with enhancer materials creates an mpUS phantom with properties that mimic normal and abnormal prostate tissue, blood vessels, and soft tissue, facilitating advanced diagnostic training and validation.

High prevalence of chromosome 17 in breast cancer micronuclei: a means to get rid of tumor suppressors?

Micronuclei (MN), defined as small extra-nuclear chromatin bodies enclosed by a nuclear envelope, serve as noticeable markers of chromosomal instability (CIN). The MN have been used for breast cancer (BC) screening, diagnosis, and prognosis. However, more recently they have gained attention as seats for active chromosomal rearrangements. BC subtypes exhibit differential CIN levels and aggressiveness. This study aimed to investigate MN chromosomal contents across BC subtypes, exploring its potential role in aggressiveness and pathogenesis. Immunostaining of BC cells was performed with anti-centromeric antibody followed by confocal microscopy. Further, fluorescence in situ hybridization (FISH) was done to check the presence of specific chromosomes in the MN. The real time PCR was also done from the RNA isolated from MN to check the expression of TP53 gene. BC cell lines (CLs) showed the presence of both centromere-positive ( +) and -negative ( -) MN, with significant variation in frequency among hormone and human epidermal growth factor receptor positive and triple-negative (TN) BC cells. FISH targeting chromosomes 1, 3, 8, 11, and 17 detected centromeric signals for all the above chromosomes in MN with a relatively higher prevalence of chromosome 17 in all the CLs. Out of all the CLs, TNBC cells demonstrated the highest frequency of centromere + and chromosome 17 + MN. TP53 expression could also be demonstrated inside the MN by FISH and real time PCR. Patient sample imprints also confirmed the presence of chromosome 17 in MN with polysomy of the same in corresponding nuclei. The high prevalence of chromosome 17 in BC MN may connote the importance of its rearrangements in the pathogenesis of BC. Further, the higher prevalence of chromosome 17 and 1 signals in TNBC MN point towards the significance of pathogenetic events involving the genes located in these chromosomes in evolution of this more aggressive phenotype.

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians’ judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.

Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variations in texture, shape, color, and size; artifacts (hairs); uneven lesion boundaries; and poor contrast. To solve these issues, this research proposes a novel Convolutional Swin Transformer (CSwinformer) method for segmenting and classifying skin lesions accurately. The framework involves phases such as data preprocessing, segmentation, and classification. In the first phase, Gaussian filtering, Z-score normalization, and augmentation processes are executed to remove unnecessary noise, re-organize the data, and increase data diversity. In the phase of segmentation, we design a new model "Swinformer-Net" integrating Swin Transformer and U-Net frameworks, to accurately define a region of interest. At the final phase of classification, the segmented outcome is input into the newly proposed module "Multi-Scale Dilated Convolutional Neural Network meets Transformer (MD-CNNFormer)," where the data samples are classified into respective classes. We use four benchmark datasets-HAM10000, ISBI 2016, PH2, and Skin Cancer ISIC for evaluation. The results demonstrated the designed framework's better efficiency against the traditional approaches. The proposed method provided classification accuracy of 98.72%, pixel accuracy of 98.06%, and dice coefficient of 97.67%, respectively. The proposed method offered a promising solution in skin lesion segmentation and classification, supporting clinicians to accurately diagnose skin cancer.

The role of miRNAs as biomarkers in cancer

MicroRNAs (miRNAs), a class of non-coding RNAs, play a critical role in regulating gene expression and have demonstrated significant potential as biomarkers in cancer research. This review explores the role of miRNAs in tumorigenesis, invasion, and metastasis, highlighting their altered regulation in various cancers, including lung, breast, liver, colorectal, and prostate cancer. miRNA expression patterns analysis helps clinicians in early cancer diagnosis, classification, and therapeutic monitoring. The stability of miRNAs in body fluids makes them ideal candidates for liquid biopsy, offering a non-invasive tool for cancer detection and prognosis assessment. Despite the promising clinical applications, challenges remain in the standardization of detection methods and integration of multi-omics data. Results are variable because different detection platforms, including qPCR, microarray and sequencing methods which have varying sensitivity and specificity. However, integrating multi-omics data comes with additional technological challenges because it calls for sophisticated bioinformatics tools to manage intricate and huge datasets. Further advancements are expected to establish miRNAs as a robust foundation for personalized cancer therapy.

Impact of observability period on the classification of COPD diagnosis timing among Medicare beneficiaries with lung cancer.

Investigators often use claims data to estimate the diagnosis timing of chronic conditions. However, misclassification of chronic conditions is common due to variability in healthcare utilization and in claims history across patients. We aimed to quantify the effect of various Medicare fee-for-service continuous enrollment period and lookback period (LBP) on misclassification of COPD and sample size. A stepwise tutorial to classify COPD, based on its diagnosis timing relative to lung cancer diagnosis using the Surveillance Epidemiology and End Results cancer registry linked to Medicare insurance claims. We used 3 approaches varying the LBP and required continuous enrollment (i.e., observability) period between 1 to 5 years. Patients with lung cancer were classified based on their COPD related healthcare utilization into 3 groups: pre-existing COPD (diagnosis at least 3 months before lung cancer diagnosis), concurrent COPD (diagnosis during the -/+ 3months of lung cancer diagnosis), and non-COPD. Among those with 5 years of continuous enrollment, we estimated the sensitivity of the LBP to ascertain COPD diagnosis as the number of patients with pre-existing COPD using a shorter LBP divided by the number of patients with pre-existing COPD using a longer LBP. Extending the LBP from 1 to 5 years increased prevalence of pre-existing COPD from ~ 36% to 51%, decreased both concurrent COPD from ~ 34% to 23% and non-COPD from ~ 29% to 25%. There was minimal effect of extending the required continuous enrollment period beyond one year across various LBPs. In those with 5 years of continuous enrollment, sensitivity of COPD classification (95% CI) increased with longer LBP from 70.1% (69.7% to 70.4%) for one-year LBP to 100% for 5-years LBP. The length of optimum LBP and continuous enrollment period depends on the context of the research question and the data generating mechanisms. Among Medicare beneficiaries, the best approach to identify diagnosis timing of COPD relative to lung cancer diagnosis is to use all available LBP with at least one year of required continuous enrollment.

Improving lung cancer diagnoses: a machine learning approach for detection and prediction in CT-Scan image analysis

Improving lung cancer diagnoses: a machine learning approach for detection and prediction in CT-Scan image analysis

Lynch syndrome diagnostic testing pathways in endometrial cancers: a nationwide English registry-based study

BackgroundFor female patients with Lynch syndrome (LS), endometrial cancer (EC) is often their first cancer diagnosis. A testing pathway of somatic tumour testing triage followed by germline mismatch repair (MMR)...

Dynamic Nanostructure-Based DNA Logic Gates for Cancer Diagnosis and Therapy.

DNA logic gates with dynamic nanostructures have made a profound impact on cancer diagnosis and treatment. Through programming the dynamic structure changes of DNA nanodevices, precise molecular recognition with signal amplification and smart therapeutic strategies have been reported. This enhances the specificity and sensitivity of cancer theranostics, and improves diagnosis precision and treatment outcomes. This review explores the basic components of dynamic DNA nanostructures and corresponding DNA logic gates, as well as their applications for cancer diagnosis and therapies. The dynamic DNA nanostructures would contribute to cancer early detection and personalized treatment.

A Low Transformed Tubal Rank Tensor Model Using a Spatial-Tubal Constraint for Sample Clustering with Cancer Multi-omics Data

Background: Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed. Objective: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer. Method: In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively. Results: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. result: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. Conclusion: The LTTRT method achieves precise segmentation of different types of cancer samples.

The impact of diagnosis and treatment of penile cancer on intimacy: a qualitative assessment.

Penile cancer is a rare malignancy (0.5-0.93/100,000 in Western countries) with significant psychosocial and sexual repercussions. This qualitative study explored the impact of penile cancer diagnosis and treatment on intimacy. A convenience sample was identified of 20 potential candidates who were at least 5 months post penile cancer surgery at a hospital centralizing penile cancer care. Participants were recruited by telephone and admitted until data saturation was reached, resulting in a sample of nine men (44-74 years old), none withdrew from participation. All interviews were performed by the same female researcher with no prior relationship to the men. The one-time interviews (35-61 min) followed a semi-structured interview guide, were audio-recorded and transcribed verbatim. Three researchers analysed the data independently using descriptive phenomenological analysis, resulting in a gradually drawn up coding tree mapping out the patient's journey. The central themes that emerged were: (1) Intimate area led to diagnostic delays, intensified diagnosis and induced secrecy; (2) Impact on sexuality prior to surgery; (3) The voyage of sexual re-discovery; (4) A partnered voyage of sexual discovery; (5) Care needs related to intimate area. This study highlights the need for comprehensive and personalized care, including pre-surgical information provision and post-surgical psychosexual support. Addressing the current unmet needs of men with penile cancer requires guidelines for psychosexual interventions and proactive efforts to reduce stigma and to raise awareness.

Living From Test to Test: A Concept Analysis of CA-125 Preoccupation in Ovarian Cancer Survivorship

Background: Ovarian cancer has a high risk of recurrence and is rarely curable. One method for monitoring recurrence is trending Cancer antigen 125 (CA-125) levels. However, treatment based on rising CA-125 levels is controversial, often contributing to anxiety and an excessive focus on test results, described as “CA-125 preoccupation.” Objective: To present an analysis on the concept of CA-125 preoccupation. Methods: Rodgers’ method of evolutionary concept analysis was used to evaluate the attributes, antecedents, and consequences. PubMed, Medline, PsycINFO, and CINAHL electronic databases were searched. Results: Antecedents include a diagnosis of ovarian cancer that necessitates CA-125 surveillance, awareness of CA-125 as a tumor marker, and perceived disease status. Attributes include psychosocial symptoms characterized by a fixation with CA-125 levels and symptoms of anxiety, depression, distress, and illness intrusiveness that present in a cyclical pattern. Consequences include urgency to treat rising levels and surveillance and survivorship care preferences. Conclusions: CA-125 preoccupation is characterized by psychosocial symptoms that present in a cyclical and dynamic state akin to living “test to test” or feeling suspended in a perpetual cycle of tests, driven by a desire to feel relief or prompt action. Implications for Practice: Nurses are poised to influence the patient’s experience through identification of the presence of CA-125 preoccupation and its consequences. Understanding the antecedents and attributes of CA-125 may assist with identifying women who are most at risk. What is Foundational: This research presents the first known conceptual definition of CA-125 preoccupation, which may lead to validated survey measures and improved survivorship care.