Search

Background. A timely diagnosis of cancer is important for patient outcomes. The delay in the patient interval (time from symptom interpretation to seeking help) is often the longest throughout the cancer patient pathway. Factors extending this interval include vague symptom profiles increasing the difficulty of symptom appraisal and individual demographics influencing help‐seeking behaviours. An underexplored and potential source of delay in cancer diagnosis is associated with managing symptoms using self‐care activities prior to presentation to healthcare. Methods. This study aimed to characterise the use of self‐care activities in the context of managing nonspecific symptoms, prior to cancer diagnosis and their effect on the length of the patient interval. Eligible publications were identified using a rapid systematic review, and their qualitative self‐care data were extracted and analysed using thematic synthesis. Results. Forty‐five qualitative research papers between 2009 and 2024 were included in the final review. Self‐care was used as part of an iterative process, often resulting in delayed presentation to healthcare, if methods were effective in managing nonspecific symptoms. Across the literature, varying types of self‐care activities were reported across all cancers with nonspecific symptoms, including the use of over‐the‐counter or alternative medications, lifestyle changes, and watchful waiting. The individual's decision to self‐care was either prompted externally by a healthcare professional (HCP) (e.g., community pharmacists) or prompted by the individual depending on the availability of home remedies and medication. Patients used self‐care when there was a low perceived need to seek healthcare, to determine whether healthcare was required, or to avoid the use of healthcare. However, across the literature, there is limited evidence to understand the variation by cancer type, symptoms, and individual characteristics. Conclusions. The findings of this rapid review demonstrate that self‐care activities could hinder prompt help‐seeking and delay cancer diagnosis among people who are experiencing nonspecific cancer symptoms. However, more evidence is needed to understand which individual factors facilitate the adoption of self‐care behaviours over prompt help‐seeking for nonspecific cancer symptoms. [ABSTRACT FROM AUTHOR]

Background/Aims. Circular RNAs (circRNAs) are often used for tumor diagnosis and treatment owing to their high stability, high expression abundance, and strong tissue specificity. The role of hsa_circ_0051908, a newly reported circRNA, in the development of hepatocellular carcinoma (HCC) is unknown. Materials and Methods. Hsa_circ_0051908 expression was determined using RT-qPCR. HCC cell proliferation, apoptosis, invasion, and migration were assessed using CCK-8 assay, EdU staining, TUNEL staining, flow cytometry, and transwell assay. The molecular mechanism was analyzed using western blotting. In addition, the role of hsa_circ_0051908 in tumor growth was evaluated in vivo. Results. Hsa_circ_0051908 expression was increased in both HCC tissues and cell lines. The proliferation, migration, and invasion of HCC cells were significantly decreased after hsa_circ_0051908 knockdown, while cell apoptosis was notably increased. Furthermore, we found that hsa_circ_0051908 silencing downregulated vimentin and Snail and upregulated E-cadherin. In vivo, hsa_circ_0051908 silencing significantly inhibited the growth of the tumor. Conclusions. Our data provide evidence that hsa_circ_0051908 promotes HCC progression partially by mediating the epithelial–mesenchymal transition process, and it may be used for HCC treatment. [ABSTRACT FROM AUTHOR]

[18F]tetrafluoroborate (TFB) has been introduced as the 18F-labeled PET imaging probe for the human sodium iodide symporter (NIS). Noninvasive NIS imaging using [18F]TFB has received much interest in recent years for evaluating various NIS-expressing tumors. Cancers are a global concern with enormous implications; therefore, improving diagnostic methods for accurate detection of cancer is extremely important. Our aim was to investigate the PET imaging capabilities of [18F]TFB in NIS-transfected lung cell line A549 and endogenous NIS-expressing tumor cells, such as thyroid cancer K1 and gastric cancer MKN45, and broaden its application in the medical field. Western blot and flow cytometry were used to assess the NIS expression level. Radioactivity counts of [18F]TFB, in vitro, in the three tumor cells were substantially higher than those in the KI inhibition group in the uptake experiment. In vivo PET imaging clearly delineated the three tumors based on the specific accumulation of [18F]TFB in a mouse model. Ex vivo biodistribution investigation showed high [18F]TFB absorption in the tumor location, which was consistent with the PET imaging results. These results support the use of NIS-transfected lung cell line A549 and NIS-expressing tumor cells MKN45 and K1, to investigate probing capabilities of [18F]TFB. We also demonstrate, for the first time, the feasibility of [18F]TFB in diagnosing stomach cancer. In conclusion, this study illustrates the promising future of [18F]TFB for tumor diagnosis and NIS reporter imaging. [ABSTRACT FROM AUTHOR]

Objective. To characterise cancer diagnosis in Scottish primary care in 2018/19 and draw comparisons with diagnostic activity in 2014. Methods. A national audit of cancer diagnosis undertaken in Scottish general practices. Participating GPs collected diagnostic pathway data on patients diagnosed with cancer in 2018/19 from medical records. These data were supplemented by linkage to the Scottish Cancer Registry and previous audit data from 2014. Analyses explored and compared patient demographics, presentation, diagnostic routes, and intervals. Results. Seventy-three practices submitted data on 2,014 cases in 2014 and 90 practices submitted data on 2,318 cases in 2018/2019. Individual demographics and types of cancer were similar. There was a higher proportion of USC (urgent suspected cancer) referrals in 2019 than 2014 (42.9% vs 38.1%, p = 0.008) but a similar proportion of emergency presentations (19.2% vs 20.4%). Primary care (median 4 (IQR 0–22) vs 5 (0–23)) and diagnostic intervals (27 (10–59) vs 30 (13–68)) were similar in both periods. Significantly fewer (24.5% vs 28.3, p = 0.015) had a diagnostic interval >60 days in 2019 than 2014. Harder to diagnose cancers were more likely to present as emergencies and be subject to prolonged delays in both cohorts. Conclusions. The 2014 and 2018/19 cohorts were broadly similar. There is limited evidence that USC use had increased between 2014 and 2018/19. Harder to diagnose cancers are still most likely to present as emergencies and be subject to delays. Overall, it seems there were small improvements in cancer diagnosis prepandemic and a further audit could examine evidence for a postpandemic recovery. [ABSTRACT FROM AUTHOR]

A 31-year-old female patient with a previous history of Carney complex and surgical resection for cardiac myxoma and bilateral adrenalectomy at 18 years old and 10 and 11 years old, respectively, was referred to our department with a diagnosis of recurrent biatrial myxomas incidentally discovered on echography. A magnetic resonance imaging (MRI) confirmed the diagnosis of a tumor protruding into the left ventricle, and the patient underwent a surgical resection of a large left atrial mass and a right-sided atrial small tumor. Diagnosis of bilateral atrial myxomas was confirmed by histologic studies. Postoperative outcome was uneventful, and the patient was discharged at the 7th postoperative day. Few months later, she reported trivial clinical symptoms suspecting a cervical radiculopathy. MRI confirmed the presence of a compressive cervical spinal cord tumoral mass at the C2-3 level leading to perform a surgical exeresis of the tumor. Histology showed a spinal melanotic schwannoma. This case highlights the risk of unexpected ubiquitary tumor locations and the importance of a rigorous transversal multidisciplinary follow-up to prevent severe complications in patients with Carney complex. [ABSTRACT FROM AUTHOR]

Objective. Staphylococcus aureus bacteremia (SAB) in patients with solid tumors poses a dilemma between infection control and cancer treatment. We aimed to explore whether early resumption of chemotherapy yielded unfavorable outcomes in oncologic patients with SAB. Methods. We retrospectively reviewed patients who received chemotherapy within 90 days of SAB onset from 2011 to 2020. We divided patients who resumed chemotherapy into two groups by the median time from the negative blood culture to the chemotherapy resumption. We investigated the association with treatment failure, which included recurrence after completion of SAB treatment, relapse during antibiotics therapy, 90-day all-cause mortality after initiation of antibiotics, and 30-day all-cause mortality after the resumption of chemotherapy. Results. Among the 78 eligible patients, 36 patients resumed chemotherapy. The median interval to the chemotherapy resumption was 17.5 days. Two patients in the early resumption group and one in the late resumption group died within 90 days after initiating antibiotics. One patient in the early resumption group experienced SAB recurrence. None of the patients experienced SAB relapse or died within 30 days of resuming chemotherapy. Conclusion. Early resumption of chemotherapy may not be directly associated with unfavorable outcomes in oncological patients with SAB under appropriate infection management. [ABSTRACT FROM AUTHOR]

The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction. [ABSTRACT FROM AUTHOR]

Background. Transducin-like enhancer of split 3 (TLE3), a member of the TLE gene family, is related to tumor genesis and progression. However, whether TLE3 played a crucial role in the whole pan-cancer remained unknown. Methods. Comprehensive analysis of TCGA, GEO, and GTEx data with an online tool, and R language was performed to explore the relationship of TLE3 expression between prognosis, gene mutation, protein phosphorylation, DNA methylation, tumor microenvironment, and related pathways in 33 tumors. Results. TLE3 was high-expressed in most tumors, and TLE3 expression and the prognosis of some tumor types were significantly correlated. The level of TLE3 expression in 33 cancer types was closely associated with DNA methylation. High-level phosphorylation sites of Tle3, such as S267 and S217, may promote cancers. In terms of the tumor microenvironment, TLE3 affected a wide variety of cancers, especially PRAD and LIHC, and TLE3 may act on them via immune-related pathways. Conclusions. The current work provided the first comprehensive investigation of TLE3 in a pan-cancer study, highlighting the role of TLE3 in the tumor immune microenvironment, and also determined the potential of TLE3 as a prognostic, immunotherapy response, and diagnostic biomarker in many cancers. However, the present results were preliminary and required further validation as this study was based on bioinformatics analyses. [ABSTRACT FROM AUTHOR]

Objective: This study aims to analyse patients' clinical profiles seen at an emergency unit of a public oncology reference hospital in Belo Horizonte, Brazil. Methods: This is a cross‐sectional, observational, retrospective study, with data collection through patients' medical records seen between 2016 and 2018. The identification of patients was carried out through research on hospital admission authorization guides. Results: A total of 2932 medical records were evaluated, of which only 21.5% were cancer patients. A cancer diagnosis was made for 21.5% of patients during hospitalisation. In this group, pain and weight loss are frequent symptoms, and haematological diseases (15.2%) were the most frequent tumour type. Patients with a previous cancer diagnosis presented the most prevalent tumour breast (18.3%) and altered consciousness level as a leading symptom. Analgesics were the most prescribed drugs in both groups. Conclusion: This study showed two distinct groups of cancer patients: with or without a previous cancer diagnosis. However, these two groups are comparable in almost all the variables analysed. Therefore, we believe that the factors that could guide the care in an emergency are related to education on cancer symptoms for the population to contribute to an early diagnosis and cancer training for emergency department (ED) professionals' to better monitor advanced‐stage patients for symptom management. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to analyse the experience of fertility preservation among female oncological patients. Methods: A phenomenological study was conducted in an Assisted Human Reproduction Unit. The sample was composed of 14 females of reproductive age diagnosed with a cancer that may affect reproductive function, who had undergone a fertility preservation procedure. In‐depth interviews were carried out, and the transcripts were entred into the Atlas.ti.v.8.2.3 qualitative analysis software package. The steps proposed by Taylor–Bogdan were followed in the analysis of the data. Results: Four thematic categories emerged in relation to the experience with the fertility preservation process: interruption of life plans and changes in the person; knowledge about fertility preservation options, reproductive capacity and impact because of the possible loss; dyadic relationshipsand social representations of fertility. Conclusions: Patients suffer from difficulties when making decisions about fertility whilst dealing with a cancer diagnosis. They need adequate information and support from health professionals. Despite increasing awareness of fertility preservation, there is a lack of knowledge regarding patients' experiences and needs related to this process. [ABSTRACT FROM AUTHOR]

Objective: The primary goal of the Campania Oncology Network (ROC) was to reduce cancer delay and care fragmentation through the establishment of cancer‐specific multidisciplinary oncologic groups (GOMs) and diagnostic and therapeutic assistance paths (PDTAs). Methods: Five cancer centres of the ROC, with their own cancer specific GOM, were selected. In our analysis, we have focused on four neoplasms: lung, colon, ovarian and prostate cancers. The median time for pre‐GOM and GOM Times was calculated for each tumour site. Univariate and multivariate logistic regressions were performed to individuate risk factors for pre‐GOM and GOM Time. Results: Significant differences were observed for prostate cancer compared to other patients either for pre‐GOM or GOM Times. Significant risks were found for ovarian and prostate cancers in pre‐GOM time and for prostate cancer in GOM‐Time. Conclusions: This experience will produce knowledge and data to guide decision‐making and to manage more effectively the challenges of fighting cancer in Campania region. The Valutazione Percorso Rete Oncologica Campana (ValPeROC) study evaluates, for the first time, the ROC activity, through the analysis of key performance indices. Pre‐GOM and GOM Time represent the quality of the entire regional health system and are useful to define models, which can evaluate the performance of the ROC over time. [ABSTRACT FROM AUTHOR]

Objective: To investigate how social context and social network activation influence appraisal and help‐seeking for symptoms potentially indicative of cancer. Methods: Semi‐structured telephone interview study. Community dwelling adults who had experienced at least one symptom potentially indicative of cancer within the last month were sampled from a national symptom survey. Results: Thirty‐four interviews were conducted. Participants looked to peers and wider society to judge whether symptoms might be normal for their age. Involvement of others in symptom appraisal promoted an active management strategy, such as contacting a healthcare professional or trying a medication. There were practical, emotional, attitudinal, normative and moral barriers to involving others. Cancer narratives from significant others, public health campaigns and the media influenced symptom appraisal. Participants held mental representations of types of people who get cancer, for example, smokers and unfit people. This had two consequences. First, participants did not identify themselves as a candidate for cancer; impeding help‐seeking. Second, social judgements about lifestyle introduced stigma. Conclusion: Involving friends/family in symptom appraisal facilitates help‐seeking but barriers exist to involving others. Campaigns to promote earlier cancer diagnosis should incorporate age‐appropriate narratives, address misconceptions about 'types' of people who get cancer and tackle stigma about lifestyle factors. [ABSTRACT FROM AUTHOR]

Pulmonary mucoepidermoid carcinoma (PMEC) is the most common malignant salivary gland tumor in the lungs and accounts for 0.1-0.2% of all lung malignancies in adults. It has no specific epidemiological or clinical characteristics. Correct diagnosis requires the combined examinations of images, laboratories, pathology, and immunohistochemistry (IHC) as well as molecular characteristics. PMEC tumors are characterized by squamous, intermediate, and mucus-secreting cells. Currently, histological appearance, mitotic frequency, cellular atypia, and necrocytosis allow the classification of PMEC into low grade or high grade. Molecular changes are crucial to pathological diagnosis. The driver of PMEC seems to be the fusion protein MECT1-MAML2 that is generated from a genetic mutation in t (11; 19) (q21; p13), while other gene mutations are also reported. However, no treatment of PMEC exists so far; surgical excision is still the primary treatment, while the efficacies of chemotherapy or radiotherapy are undefined. Tyrosine kinase inhibitor (TKI) therapy and immunotherapy showed to have significant therapeutic effects but require more investigation and better understanding. This review focuses on the clinical characteristics, imaging and pathologic features, immunohistochemical examination, mutation analysis, differential diagnosis, prognosis, and treatment of PMEC. [ABSTRACT FROM AUTHOR]

Background. Tissue inhibitor of metalloproteinase-2 (TIMP2), an endogenous inhibitor of matrix metalloproteinases, has been disclosed to participate in the development and carcinogenesis of multiple malignancies. However, the prognosis of TIMP2 in different cancers and its correlation with tumor microenvironment and immunity have not been clarified. Methods. In this study, we conducted a comprehensive bioinformatics analysis to evaluate the prognostic and therapeutic value of TIMP2 in cancer patients by utilizing a series of databases, including Oncomine, GEPIA, cBioPortal, GeneMANIA, Metascape, and Sangerbox online tool. The expression of TIMP2 in different cancers was analyzed by Oncomine, TCGA, and GTEx databases, and mutation status of TIMP2 in cancers was then verified using the cBioPortal database. The protein-protein interaction (PPI) network of the TIMP family was exhibited by GeneMANIA. The prognosis of TIMP2 in cancers was performed though the GEPIA database and Cox regression. Additionally, the correlations between TIMP2 expression and immunity (immune cells, gene markers of immune cells, TMB, MSI, and neoantigen) were explored using Sangerbox online tool. Results. The transcriptional level of TIMP2 in most cancerous tissues was significantly elevated. Survival analysis revealed that an elevated expression of TIMP2 is associated with unfavorable survival outcome in multiple cancers. Enrichment analysis demonstrated the possible mechanisms of TIMPs and their associated genes mainly involved in pathways including extracellular matrix (ECM) regulators, degradation of ECM and ECM disassembly, and several other signaling pathways. Conclusions. Our findings systematically dissected that TIMP2 is a potential prognostic maker in various cancers and use the inhibitor of TIMP2, which may be an effective strategy for cancer therapy to improve the poor cancer survival and prognostic accuracy, but concrete mechanisms need to be validated by subsequent experiments. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the association of sleep duration with health‐related quality of life (HRQoL) and mental health in cancer survivors. Design and Methods: This cross‐sectional study included 846 cancer survivors aged 19 years and older from Korea. Associations between sleep duration, HRQoL, and mental health were analyzed using analysis of covariance and logistic regression. Findings: Survivors who slept 5 h or less had lower HRQoL and higher risk of suicidal ideation than survivors who slept 6–8 h. Practice Implications: Sleep improvement programs should be developed as sleep disorders may be a central concern among cancer survivors. [ABSTRACT FROM AUTHOR]

Unicystic mucoepidermoid carcinoma (UC-MEC) is a rare MEC variant, and its diagnosis is frequently problematic. This study is aimed at summarizing its clinicopathologic characteristics, treatment, and prognosis and proposing key points to avoid missed diagnosis and misdiagnosis in clinical and pathological conditions. This retrospective study included 30 UC-MEC cases, and the clinical findings were collected from the clinical medical records. Radiographic features, histologic behaviors, MAML2 rearrangement by fluorescence in situ hybridization (FISH), and follow-up data were analyzed. Moreover, glandular odontogenic cyst (GOC) and cytadenoma (CA) were used as controls. In the UC-MEC group, 19 patients were female (63%), and 11 were male (37%). The mean patient age was 39.5 (range, 7–72 years). The affected locations included the jaw (8 maxillary, 3 mandibular) and salivary glands (7 parotid, 11 palates, and 1 floor of the mouth). The chief complaint was swelling; the lesions were all cystic, among which 66.7% were well circumscribed and 33.3% poorly defined. Microscopic examination showed two UC-MEC histologic subtypes. Type A presented as a single cyst with mural thickening (8/30, 27%) lined predominantly by epidermoid cells with interspersed intermediate and mucinous cells, and type B (22/30, 73%) showed infiltrative tumor islands in the cystic wall or the surrounding tissue. FISH analysis suggested that approximately 66.7% of UC-MEC harbored a MAML2 rearrangement. During the median follow-up period of 42 months (range, 6–120 months), all type A patients and 68% of type B patients who underwent complete surgical resection lived without relapse. Seven cases with type B cancer that underwent curettage initially had local recurrence. Clinicians and pathologists hardly recognize UC-MEC owing to its cystic architecture. Specific epidermoid, mucous, and intermediate tumor cells, and MAML2 fusion testing, are essential to avoid potential diagnostic pitfalls. Prompting and completing resection surgery with negative margins would have a favorable prognosis. [ABSTRACT FROM AUTHOR]

Brain tumor is regarded as one of the fatal and dangerous diseases on the planet. It is present in the form of uncontrolled and irregular cells in the brain of an infected individual. Around 60% of glioblastomas turn into large tumors if it is not diagnosed earlier. Some valuable literature is available on tumor diagnosis, but there is room for improvement in overall performance. Machine Learning (ML)-based techniques have been widely used in the medical domain for early diagnostic diseases. The use of ML techniques in conjunction with improved image-guided technology may help in improving the performance of the brain tumor detection process. In this work, an ML-based brain tumor detection technique is presented. Adaptive Back Propagation Neural Network (ABPNN) and Support Vector Machine (SVM) algorithms are used along with fuzzy logic. The fuzzy logic is used to fuse the result of ABPNN and SVM. The proposed technique is developed using the BRATS dataset. Experimental results reveal that the ABPNN model achieved 98.67% accuracy in the training phase and 96.72% accuracy in the testing phase. On the other hand, the SVM model has attained 98.48% and 97.70% accuracy during the training and testing phases. After applying fuzzy logic for decision-based fusion, the overall accuracy of the proposed technique reaches 98.79% and 97.81% for the training and the testing phases, respectively. The comparative analysis with existing techniques shows the supremacy of the proposed technique. [ABSTRACT FROM AUTHOR]

BK virus-associated nephropathy (BKVN) remains a major infectious complication due to powerful immunosuppression in kidney transplant recipients, and its histologic appearance can mimic rejection, leading to diagnostic and treatment dilemmas thus molecular diagnostic methods would be beneficial. We collected gene expression profiles of 169 kidney biopsies taken from BKVN, rejection, and stable functioning allografts, based on single sample gene set enrichment analysis and random forest algorithm, and three hallmark activities associated with DNA damage and proliferation were found to be relatively specific in BKVN. Subsequently, weighted gene co-expression network analysis and support vector machines (SVM) algorithm identified RBBP7 as a robust and promising biomarker with high accuracy in both training and validation cohorts (AUC =0.938, 0.977, respectively). Besides, potential drugs for BKVN treatment such as mepacrine were discovered, which may contribute to targeted antiviral therapy and effective patient management rather than simply reducing the doses of immunosuppressive agents in clinical practice. RBBP7 (retinoblastoma binding protein 7) serves as component of serval complexes that regulate chromatin metabolism and functions in affecting DNA replication and controlling cell proliferation. In this research, upregulation of RBBP7 was found to be associated with the higher infiltration of CD8 naïve T, iTreg, and neutrophil cells and the lower amounts of Th1, central memory T, NKT, CD8 T, and dendritic cells. Moreover, the infiltration of Th1, Th17, and NKT cells was steadily different between BKVN and rejection allografts through immune cell assessment. In conclusion, we identified and verified RBBP7 as a molecular biomarker for BKVN diagnosis, which demonstrated great distinguishing ability with allograft rejection and would support clinical decision-making. [ABSTRACT FROM AUTHOR]

Background. Cancers of digestive system have high case-fatality rate. It is important to find more appropriate methods in diagnosing and predicting gastrointestinal malignances. And thrombospondin-2 (TSP-2) was reported to have the functions, although results were not identical. So we performed this meta-analysis to clarify the significance of TSP-2 in this area. Methods. PubMed, Embase, Web of Science, Cochrane Library, and Clinicaltrial.gov were searched for relevant studies. Data were extracted from these involved records. For the meta-analysis of diagnostic test, bivariate mixed effect model was used to estimate diagnostic accuracy. For prognosis part, HRs and their 95% CIs were pooled to compare the overall survival (OS) and disease-free survival (DFS) between patients with high TSP-2 and low TSP-2. Results. Nine records were eligible for the analysis of diagnostic test. Pooled results were as follows: sensitivity 0.60 (0.52, 0.68), specificity 0.96 (0.91, 0.98), positive likelihood ratio (PLR) 15.4 (7.3, 32.2), negative likelihood ratio (NLR) 0.42 (0.34, 0.50), and diagnostic odds ratio (DOR) 37 (18, 76). While in prognosis part, 10 articles were included. Patients with increased TSP-2 had shorter OS (HR = 1.64 , 95% CI = 1.21 -2.22); however, no difference was found in DFS between TSP-2 high and low groups (HR = 1.44 , 95% CI = 0.28 -7.33). Conclusions. TSP-2, as a diagnostic marker, has a high specificity but a moderate sensitivity. Meanwhile, it plays a role in predicting OS. Therefore, making TSP-2 a routine assay could be beneficial to high-risk individuals and patients with digestive malignances. [ABSTRACT FROM AUTHOR]

The incidence and mortality rates are increasing year by year, and the incidence of the disease is gradually becoming younger. The purpose of this study was to investigate the clinical diagnostic value of PACS in breast tumor patients. Methods. 20 patients with breast tumor diagnosed by PACS were selected for the study, and the diagnosis was confirmed by pathological puncture or surgery. Results. The detection rates of breast tumor by MRI and CT were 94.44% and 96.67%, the sensitivities were 18.82% breast tumor and 96.67%, and the specificities were 53.84% and 54.54%, with no statistically significant difference (P > 0.05). There was no statistically significant difference in the detection rate of invasive lobular carcinoma (LDC) and PACS (P > 0.05). Conclusion. PACS has a greater detection rate for breast tumor and offers some diagnostic usefulness in diagnosing malignant breast tumor. The detection rate of breast tumors can be increased by selecting the most appropriate diagnostic tool for the patient's current circumstances. [ABSTRACT FROM AUTHOR]

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the application of computed tomography (CT) images in the diagnosis of gastric tumor under the intelligent reconstruction algorithm (IRA). 120 patients with gastric cancer were selected and all the patients underwent CT scanning, and CT images were analyzed based on the Feldkamp-Davis-Kress algorithm (FDK algorithm) to evaluate the imaging features of gastric lesions. According to biopsy or surgical pathology, the detection rate of CT images was calculated. The results showed that there were three pathological types of benign tumors (polyps, leiomyomas, and mesenchymomas) and three pathological types of malignant tumors (mesenchymomas, adenomas, and lymphomas). In addition, the detection rates of CT scans were different, reaching 94.2% on different orientations of the stomach, 90.7% of benign tumors, and 90.9% of malignant tumors, so the detection rate of different orientations was relatively high. CT images based on the FDK IRA could realize a high detection rate in diagnosis, accurately locate the lesion, and display the characteristics of the lesion and the metastasis of surrounding tissues; there were significant differences between benign and malignant gastric tumors in CT images, and the detection effect was obvious, which is worthy of clinical application and promotion. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients' health report and provides better treatment. [ABSTRACT FROM AUTHOR]

In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the residual block is introduced into U-Net network for improvement to avoid the degradation of model performance caused by the gradient disappearance and reduce the training difficulty of deep network. At the same time, considering the features of spatial and channel attention, a fusion attention mechanism is proposed to be introduced into the image analysis model to improve the ability to obtain the feature information of ultrasound images and realize the accurate recognition and extraction of breast tumors. The experimental results show that the Dice index value of the proposed method can reach 0.921, which shows excellent image segmentation performance. [ABSTRACT FROM AUTHOR]

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. [ABSTRACT FROM AUTHOR]

The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liver tumors. Fifty-eight patients with liver tumors were selected, and their CT images were segmented using a convolutional neural network (CNN) algorithm. The segmentation results were quantitatively evaluated using the Dice similarity coefficient (DSC), precision, and recall. All the patients were examined and diagnosed by CT enhanced delayed scan technique, and the CT scan results were compared with the pathological findings. The results showed that the DSC, precision, and recall of the CNN algorithm reached 0.987, 0.967, and 0.954, respectively. The images segmented by the CNN were clearer. The diagnostic result of the examination on 56 cases by CT enhanced delay scanning was consistent with that of pathological diagnosis. According to the result of pathological diagnosis, there were 6 cases with hepatic cyst, 9 with hepatic hemangioma, 12 cases with liver metastasis, 10 cases with hepatoblastoma, 3 cases with focal nodular hyperplasia, and 18 cases with primary liver cancer. The result of CT enhanced delay scanning on 58 patients was consistent with that of pathological diagnosis, and the total diagnostic coincidence rate reached 96.55%. In conclusion, the CNN algorithm can perform accurate and efficient segmentation, with high resolution, providing a more scientific basis for the segmentation of liver tumors in CT images. CT enhanced scanning technology has a good effect on the diagnosis and differentiation of liver tumor patients, with high diagnostic coincidence rate. It has important value for the diagnosis of liver tumor and is worthy of clinical application. [ABSTRACT FROM AUTHOR]

Objective. To analyze the consistency of preoperative CTLM imaging in the diagnosis of breast cancer lesions and postoperative pathological examination. Methods. The clinical data of 225 patients with breast tumor in our breast surgery department were collected. All patients underwent mammography, CTLM, and pathological examination. To analyze the image characteristics of breast CTLM imaging, calculate the diagnostic efficacy of CTLM imaging for breast tumors, and compare the image characteristics of CTLM imaging for benign and malignant tumors. Results. (1) Postoperative pathological examination showed that 136 cases (60.44%) of lesions were benign tumors, and 89 cases (39.56%) were malignant tumors. (2) The "spokes distribution" of normal breast CTLM images was interrupted. In the 3D reconstructed images, the morphology of the abnormal angiogenesis area is mostly irregular nonbanded structure, which is manifested as slab structure, spindle structure, spherical structure, diverticulum structure, inverted conical structure, rings structure, branched structure, and dumbbell structure. (3) The detection rate of breast tumor by CTLM imaging was 84.44%. The specificity and coincidence rate of CTLM imaging were higher than that of mammography (P < 0.05). (4) The features of CTLM imaging images of breast malignant tumors are mostly bright white locally, with irregular edges and obvious attenuation of laser signal, and the reconstructed shape of 3D images is mostly like a slab structure. Conclusion. CTLM imaging can provide related information of neovascularization in breast cancer lesions, which is basically consistent with pathologically confirmed lesions. [ABSTRACT FROM AUTHOR]

Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods. A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results. Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion. The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. [ABSTRACT FROM AUTHOR]

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze the value of artificial intelligence algorithm-based computerized tomography (CT) image combined with serum tumor markers for diagnoses of pancreatic cancer. In the study, 68 hospitalized patients with pancreatic cancer were selected as the experimental group, and 68 hospitalized patients with chronic pancreatitis were selected as the control group, all underwent CT imaging. An image segmentation algorithm on account of two-dimensional (2D)-three-dimensional (3D) convolution neural network (CNN) was proposed. It also introduced full convolutional network (FCN) and UNet network algorithm. The diagnostic performance of CT, serum carbohydrate antigen-50 (CA-50), serum carbohydrate antigen-199 (CA-199), serum carbohydrate antigen-242 (CA-242), combined detection of tumor markers, and CT-combined tumor marker testing (CT-STUM) for pancreatic cancer were compared and analyzed. The results showed that the average Dice coefficient of 2D-3D training was 84.27%, which was higher than that of 2D and 3D CNNs. During the test, the maximum and average Dice coefficient of the 2D-3D CNN algorithm was 90.75% and 84.32%, respectively, which were higher than the other two algorithms, and the differences were statistically significant (P < 0.05). The penetration ratio of pancreatic duct in the experimental group was lower than that in the control group, the rest were higher than that in the control group, and the differences were statistically significant (P < 0.05). CA-50, CA-199, and CA-242 in the experimental group were 141.72 U/mL, 1548.24 U/mL, and 83.65 U/mL, respectively, which were higher than those in the control group, and the differences were statistically significant (P < 0.05). The sensitivity, specificity, positive predictive value, and authenticity of combined detection of serum tumor markers were higher than those of CA-50, CA-199, and CA-242, and the differences were statistically significant (P < 0.05). The results showed that the proposed algorithm 2D-3D CNN had good stability and image segmentation performance. CT-STUM had high sensitivity and specificity in diagnoses of pancreatic cancer. [ABSTRACT FROM AUTHOR]

This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patients with breast tumor were included in this study, all of whom underwent routine ultrasound examinations. Subsequently, TUI was used to assist in guiding the positioning, and the light scattering tomography system was used to further measure the lesions. A deep learning model was established to process the imaging results, and the pathological test results were undertaken as the gold standard for the efficiency of different imaging methods to diagnose the breast tumors. The results showed that, among 120 patients with breast tumor, 56 were benign lesions and 64 were malignant lesions. The average total amount of hemoglobin (HBT) of malignant lesions was significantly higher than that of benign lesions (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of TUI in the diagnosis of breast cancer were 90.4%, 75.6%, 81.4%, 84.7%, and 80.6%, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of ultrasound in the diagnosis of breast cancer were 81.7%, 64.9%, 70.5%, 75.9%, and 80.6%, respectively. In addition, for suspected breast malignant lesions, the combined application of ultrasound and tomography can increase the diagnostic specificity to 82.1% and the accuracy to 83.8%. Based on the above results, it was concluded that TUI combined with ultrasound had a significant effect on benign and malignant diagnosis of breast cancer and can significantly improve the specificity and accuracy of diagnosis. It also reflected that deep learning technology had a good auxiliary role in the examination of diseases and was worth the promotion of clinical application. [ABSTRACT FROM AUTHOR]

[18F]tetrafluoroborate (TFB) has been introduced as the 18F-labeled PET imaging probe for the human sodium iodide symporter (NIS). Noninvasive NIS imaging using [18F]TFB has received much interest in recent years for evaluating various NIS-expressing tumors. Cancers are a global concern with enormous implications; therefore, improving diagnostic methods for accurate detection of cancer is extremely important. Our aim was to investigate the PET imaging capabilities of [18F]TFB in NIS-transfected lung cell line A549 and endogenous NIS-expressing tumor cells, such as thyroid cancer K1 and gastric cancer MKN45, and broaden its application in the medical field. Western blot and flow cytometry were used to assess the NIS expression level. Radioactivity counts of [18F]TFB, in vitro, in the three tumor cells were substantially higher than those in the KI inhibition group in the uptake experiment. In vivo PET imaging clearly delineated the three tumors based on the specific accumulation of [18F]TFB in a mouse model. Ex vivo biodistribution investigation showed high [18F]TFB absorption in the tumor location, which was consistent with the PET imaging results. These results support the use of NIS-transfected lung cell line A549 and NIS-expressing tumor cells MKN45 and K1, to investigate probing capabilities of [18F]TFB. We also demonstrate, for the first time, the feasibility of [18F]TFB in diagnosing stomach cancer. In conclusion, this study illustrates the promising future of [18F]TFB for tumor diagnosis and NIS reporter imaging. [ABSTRACT FROM AUTHOR]

Objective. To analyze the diagnosis and treatment of patients with concomitant malignant tumors after organ transplantation by compiling data from organ transplantation patients. Methods. By searching CNKI and PubMed databases, we made a systematic analysis of the studies of postorgan transplantation complicating malignant tumors in the last decade. Results. There were 10 articles on malignant tumors after renal transplantation, 8 articles on liver transplantation, 2 articles on heart transplantation, and 1 article on lung transplantation. The incidence of malignant tumors complicating renal transplantation is 10.4% in Europe, with skin cancer and Kaposi's sarcoma being common; the incidence in the United States is 3.4%, with PTLD having the highest incidence; the incidence of malignant tumors is relatively lowest in Asia, with gastrointestinal malignancies being the main ones. The mean time to complication of malignancy after renal transplantation is 3.83 years. The incidence of concurrent malignancies after liver transplantation is 8.8% in Europe, where skin cancer and Kaposi's sarcoma are common; 5.6% in Asia, where gastrointestinal tract tumors are prevalent; and 4.5% in the United States, where gastrointestinal tract tumors, PTLD, and hematologic diseases are predominant. The mean time to complication of malignancy after liver transplantation is 4.79 years. The incidence of malignancy after heart transplantation is 6.8-10.7%. The incidence of malignancy after lung transplantation is about 10.1%. Minimization of immunosuppression or modification of immunosuppression regimens may be a key component of cancer prevention. mTOR inhibitors and phenolate (MMF) reduce the incidence of de novo malignancies in patients after solid organ transplantation. Surgical treatment improves survival in patients with early malignancies. The use of external beam radiation therapy in the treatment of hepatocellular carcinoma is limited due to the risk of radiation liver disease. Conclusions. The risk of concomitant malignancy needs to be guarded for 5 years of immunosuppressive therapy after organ transplantation surgery. Adjusting the immunosuppressive treatment regimen is an effective way to reduce concurrent malignancies. Systemic chemotherapy or radiotherapy requires vigilance against the toxic effects of drug metabolism kinetics on the transplanted organ. [ABSTRACT FROM AUTHOR]

Cancer survivors suffer a higher risk of coronary artery atherosclerosis (CAA). Whether cancer patients had increased baseline CAA burden prior to cardiotoxic therapy remains unclear. We conducted a case-control study, and 286 consecutive patients were finally included. Among these patients, 181 had newly diagnosed cancer and 105 had nonmalignant diseases. Cancer was confirmed by biopsy. The severity of CAA was determined by coronary angiography and evaluated using the percentage of stenosis or Gensini scoring (GS). Patients with cancer versus cancer-free controls were older (OR = 1.052, 95% CI: 1.021–1.084, p < 0.001), more commonly male (OR = 0.048, 95% CI: 1.004–2.676, p = 0.048), and more severely exposed to smoking (OR = 1.020, 95% CI: 1.007–1.033, p = 0.003). Cancer patients were significantly more commonly complicated by ≥90% coronary stenosis than the gender- and age-matched cancer-free controls (9/93 versus 1/93, OR = 4.875, 95% CI: 1.024–23.213, p = 0.047). After adjustment for age, gender, hypertension, diabetes, smoking history, blood glucose, and total cholesterol, cancer was significantly associated with high GS (adjusted OR = 2.208, 95% CI: 1.077–4.524, p = 0.031). Our study demonstrated that cancer patients had increased CAA burden prior to cardiotoxic therapy. Further study is necessary to investigate the link between CAA and cancer. [ABSTRACT FROM AUTHOR]

In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm. [ABSTRACT FROM AUTHOR]

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value. [ABSTRACT FROM AUTHOR]

Objective: Timely diagnosis of cancer is important for prognosis. Patients' health literacy (HL) may impact differences of diagnostic delays. Thus, we aim to explore the association between HL and diagnostic intervals in cancer. Methods: Questionnaire data were obtained from patients with cancer diagnosed in 2016 and their general practitioner (n = 3890). The primary care interval (PCI) and the diagnostic interval (DI) were calculated using dates from national registries and questionnaires. A long PCI and DI were defined as ≥75th percentile. HL was assessed using scales from the Health Literacy Questionnaire: engaging with healthcare providers (scale 6:Engagement, n = 3008), navigating in healthcare (scale 7:Navigation, n = 2827) and understanding health information (scale 9:Knowledge, n = 3002). Low HL was defined as a score ≤3. Results: Low HL was reported by 12.2% (Engagement), 27.0% (Navigation) and 9.3% (Knowledge) of the patients and associated with a long PCI after adjustment of socio‐economic factors: Engagement (prevalence rate ratio [PRR] 1.27 [95% CI 1.01–1.60]), Navigation (PRR 1.41 [95% CI 1.10–1.80]) and Knowledge (PRR 1.32 [95% CI 1.03–1.68]). No association was found between HL and DI. Conclusion: HL may interfere with the diagnostic processes in general practice. Efforts to manage low HL include GPs' awareness of patients' ability to explain and respond to bodily changes and use of corresponding safety‐netting strategies. [ABSTRACT FROM AUTHOR]

Increasing evidence indicated that the tumor microenvironment (TME) played a crucial role in cancer initiation and progression. Ubiquitin-conjugating enzyme E2C (UBE2C) was differentially expressed in many cancer types. However, the immunological and prognostic roles of UBE2C were unclear. Differentially expressed genes (DEGs) of 29 cancer types were downloaded from GEPIA2 and 4 cancer types failed to download owing to no DEGs. Furthermore, the gene expression profiles, mutation data, and survival data of 33 cancer types were obtained from UCSC Xena. Clinical stage relevance, tumor mutational burden (TMB), TME relevance analysis, and gene set enrichment analysis (GSEA) of DEGs in 33 cancer types were performed. And DEGs were identified in oral squamous cell carcinoma (OSCC) by biological experiments. Previous studies indicated that UBE2C was related to the prognosis of many cancers. In our study, the higher UBE2C expression level meant a terminal clinical stage in 8 cancer types and the expression level of UBE2C was related to TMB in 20 cancer types. In addition, both immune relevance analysis and GSEA showed that UBE2C might participate in immune response in many cancers. Furthermore, the UBE2C mRNA level and protein level were all identified as upregulated in OSCC cell lines and tissues. UBE2C was differentially expressed in many cancer types and related to the pathogenesis and TME of many cancers, which might be a potential diagnostic and therapeutic biomarker. [ABSTRACT FROM AUTHOR]

Various factors influencing postoperative incisional infection in gynecologic tumors were analyzed, and the value of quality nursing intervention was studied. In this study, 74 surgically treated gynecologic tumor patients were randomly selected from within the hospital as the study population and were divided into study and control groups. For this purpose, the whole-group random sampling method is utilized to compare the postoperative incisional infection rates of the two groups, analyze their influencing factors, and develop quality nursing interventions. In this paper, a breast cancer diagnosis prediction model was developed by combining the self-attentive mechanism. The preprocessing work such as data quantification and normalization was performed first which is followed by adding the preprocessed data to the self-attentive mechanism. This model has solved the problem that recurrent neural networks (RNNs) could not extract and calculate the features at the same time. Likewise, it has solved the drawback that the RNN could not consider global features at the same time when extracting the features, and then, the feature matrix extracted by the self-attentive mechanism was added to the adaptive neural network. The adaptive neural network model for breast cancer diagnosis prediction was constructed and, finally, relevant parameters of the adaptive neural network model were adjusted according to different tasks to make the model performance optimal. Experimental results showed that the postoperative incision infection rate of patients in the study group was 2.70%, which was significantly lower than that of 21.62% in the control group (P < 0.05). Likewise, operation time, operation method, hospitalization time, preoperative fever, diabetes mellitus, and anemia were the main influencing factors of postoperative incision infection in women with gynecologic tumors. The time of surgery, surgical method, long hospital stay, preoperative fever, diabetes, and anemia are the main factors that lead to postoperative incisional infection in female gynecologic tumor patients. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of thyroid cancer (TC) patients has gradually increased, and it ranks first among all endocrine tumors. TC has no obvious characteristics at the initial stage of onset. Thyroid tumors (TT) have formed when they are discovered, and they are easy to see when they are diagnosed. The disease is confused, so it is necessary to rely on imaging methods for tumor diagnosis. Contrast-enhanced ultrasound (CEUS), as the most commonly used imaging method in current clinical testing, is simple, safe, highly sensitive, can accurately display tumor conditions, and has high clinical value in the judgment of TC tumors. This article uses meta-analysis to select 63 published studies on CEUS to determine benign and malignant (BAM) TT to analyze and explore its clinical application value. This article understands the analysis of BAM TT and its diagnostic methods, clarifies the diagnostic efficiency of CEUS for TT, imaging methods, and imaging characteristics, and uses statistical analysis to analyze its heterogeneity. In this paper, the meta-analysis of CEUS in judging BAM TT is mainly based on references. The sensitivity, specificity, and difference of CEUS in diagnosing BAM TT are analyzed. Real-time elastography (RTE) is the comparison experiment object, and CEUS is used to compare the diagnostic efficiency, pathological results, and diagnostic efficiency of thyroid nodules in CEUS mode. The results of the study show that the nodule with higher diagnostic sensitivity is the echo feature, with a sensitivity of 97.73%, followed by the halo feature, with a sensitivity of 86.36%. In terms of diagnostic specificity, the boundary feature is the most specific. The specificity is 89.47%. In the judgment of BAM tumor nodules, the most obvious difference is the echo feature, which is as high as 14.09, followed by the acoustic halo feature, and the difference is 10.65. [ABSTRACT FROM AUTHOR]

The goal of this study was to see if automatic biopsy guided by ultrasound could be used to provide a qualitative diagnosis of a liver tumor. Methods. A total of 40 patients (101 focuses) were treated with automatic liver parenchyma biopsy under ultrasound guidance, and the correlation between pathological outcomes and ultrasound images was investigated. The lesion size in the observation group was compared to that in the control group using conventional ultrasound (P > 0.05), and there was no significant difference. Under contrast-enhanced ultrasound (CEUS), there was no statistically significant difference in lesion size between the observation and control groups (P > 0.05). The difference in lesion size between the conventional ultrasonography and CEUS observation groups was statistically significant (P 0.05). Conclusion. Ultrasound-guided automated biopsy of the liver parenchyma is a simple and effective procedure with fewer problems and a high diagnostic rate, and it deserves to be promoted clinically. [ABSTRACT FROM AUTHOR]

Objective. To study computed tomography (CT) imaging characteristics of bladder tumors, to explore the value of CT in tumor diagnosis, and to identify the relevant factors of CT missed diagnosis so that medical staff can be more accurate in the diagnosis of bladder tumors. Methods. To retrospectively analyze the CT manifestations of 153 bladder tumor cases confirmed by paraffin pathology in our hospital and to study the difference between the benign and CT imaging features. CT indicators mainly include the number, location, morphology, calcification, bladder wall smoothness, CT value, degree of enhancement, and invasion of surrounding tissues and organs. Then, we retrospectively analyze 17 cases of CT missed diagnosis of bladder tumors, analyze related factors, and discuss the role of CT in the diagnosis of bladder tumors. Results. This study has shown that with the help of CT images, the diagnosis rate of bladder tumors has been greatly improved. Of the 153 patients studied, noninvasive urothelial carcinoma accounted for 18.95% of all benign and malignant bladder tumors, invasive urothelial carcinoma accounted for 67.93%, prostatic metastatic carcinoma and inflammatory myofibroblastoma accounted for 8.47%, pheochromocytoma accounted for 1.31%, inverted papilloma accounted for 1.31%, tubular choriocarcinoma accounted for 0.63%, and endocystitis accounted for 1.31%. In addition, the blood supply level, CT index bladder wall smoothness, and CT value are also statistically significant (P < 0.05). Conclusions. CT is of high value in the diagnosis of bladder tumors, and benign and malignant bladder tumors have CT and CT imaging features. The size of bladder tumors is related to the missed diagnosis rate of CT. The application of CT examination technology can improve the accuracy of diagnosis of bladder tumors. [ABSTRACT FROM AUTHOR]

This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P < 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors. [ABSTRACT FROM AUTHOR]

Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

This study was to explore the clinical application value of computed tomography (CT) images based on a three-dimensional (3D) reconstruction algorithm for laparoscopic partial nephrectomy (LPN) in patients with renal tumors. 30 cases of renal cell carcinoma (RCC) patients admitted to the hospital were selected as the research objects and were rolled into two groups using a random table method. The patients who received PLN under the three-dimensional reconstruction and laparoscopic technique were included in the experimental group (group A), and the patients who received LPN using CT images only were included in the control group (group B). In addition, the treatment results of the two groups of patients were compared and analyzed. Results. The effective rate of the established model was 93.3%; the total renal arteriovenous variability of group A (13.3%) was higher than that of group B (6.7%), and the operation time (131.5 ± 32.1 minutes) was much lower than that of group B (158.7 ± 36.2 minutes), showing statistical significance (P < 0.05). Conclusion. CT images based on 3D reconstruction algorithms had high clinical application value for LPN in patients with renal tumors, which could improve the efficiency and safety of LPN. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis and treatment are key to reducing deaths from cancer, but people from Black and Minority Ethnic (BME) groups are more likely to encounter delays in entering the cancer care system. Roma, Gypsies and Travellers are ethnic minorities who experience extreme health inequalities. Objective: To explore the experiences of cancer diagnosis, treatment and care among people who self‐identify as Roma or Gypsies and Travellers. Methods: A participatory qualitative approach was taken. Peer researchers conducted semi‐structured interviews (n = 37) and one focus group (n = 4) with community members in Wales and England, UK. Results: Cancer fatalism is declining, but Roma, Gypsies and Travellers experience barriers to cancer healthcare at service user, service provider and organisational levels. Communication was problematic for all groups, and Roma participants reported lack of access to interpreters within primary care. Clear communication and trusting relationships with health professionals are highly valued and most frequently found in tertiary care. Conclusion: This study suggests that Roma, Gypsies and Travellers are motivated to access health care for cancer diagnosis and treatment, but barriers experienced in primary care can prevent or delay access to diagnostic and treatment services. Organisational changes, plus increased cultural competence among health professionals, have the potential to reduce inequalities in early detection of cancer. [ABSTRACT FROM AUTHOR]

Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method. [ABSTRACT FROM AUTHOR]

This study aimed to compare the role of magnetic resonance imaging (MRI) and computed tomography (CT) images based on the low-rank matrix (LRM) denoising (LRMD) algorithm in the diagnosis of cerebral aneurysms (CAs). By comparing the role of MRI and CT in the diagnosis of CA, it would be helpful to formulate more reasonable diagnosis strategies and provide a solid foundation for clinical treatment of patients. 80 patients with cerebral aneurysm admitted to hospital were selected as the research objects. First, the LRMD algorithm was established and applied to the image denoising process of MRI and CT. Then, the diagnosis rate of CA by MRI and CT before and after denoising was compared, and the diagnostic rates of the two methods for aneurysms of different sizes were compared. Finally, the location, foci, and patient satisfaction of the aneurysm were compared. The results showed that the MRI and CT images after denoising with LRM were clearer, and the secondary structures in the brain were more obvious. It meant that LRMD had good image denoising effect. The diagnostic rate of denoised MRI and CT was improved. Although the difference was not statistically notable, the diagnostic rate of CT was obviously higher in contrast to MRI (P < 0.05). The diagnostic rate of CT for smaller aneurysms (<3 mm and 3–5 mm) was also notably higher in contrast to MRI (P < 0.05). However, there was no difference in the diagnosis of tumor location between the two. The clarity of CT diagnostic images was better than MRI (P < 0.05). Accordingly, patients were more satisfied with CT in contrast to MRI (P < 0.05). In summary, CT images based on the LRMD algorithm were superior to MRI in the diagnosis of CA, and it could provide more accurate diagnosis results. [ABSTRACT FROM AUTHOR]

The fabrication of sensitive protein microarrays such as PCR used in DNA microarray is challenging due to lack of signal amplification. The development of microarrays is utilized to improve the sensitivity and limitations of detection towards primal cancer detection. The sensitivity is enhanced by the use of ZnO-nanorods and is investigated as a substrate which enhance the florescent signal to diagnose the hepatocellular carcinoma (HCC) at early stages. The substrate for deposition of ZnO-nanorods is prepared by the conventional chemical bath deposition method. The resultant highly dense ZnO-nanorods enhance the fluorescent signal 7.2 times as compared to the substrate without ZnO-nanorods. The microarray showed sensitivity of 1504.7 ng ml-1 and limit of detection of 0.1 pg ml-1 in wide dynamic range of 0.05 pg-10 μg ml-1 for alpha fetoprotein (AFP) detection in 10% human serum. This immunoassay was successfully applied for human serum samples to detect tumor marker with good recoveries. The ZnO-nanorod substrate is a simple protein microarray which showed a great promise for developing a low-cost, sensitive, and high-throughput protein assay platform for several applications in both fundamental research and clinical diagnosis. [ABSTRACT FROM AUTHOR]

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors. [ABSTRACT FROM AUTHOR]