According to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from breast cancer (BC) each year, making it the second-highest cause of mortality for women globally. In recent years, advances in artificial (AI) intelligence and machine (ML) learning have shown incredible potential in increasing the accuracy and efficiency of BC diagnosis. This research describes an intelligent BC image analysis system that leverages the capabilities of transfer learning (TLs) with ensemble stacking ML models. As part of this research, we created a model for analyzing ultrasound BC images using cutting-edge TL models such as Inception V3, VGG-19, and VGG-16. We have implemented stacking of ensemble ML models, including MLP (Multi-Layer Perceptron) with different architectures (10 10, 20 20, and 30 30) and Support Vector Machines (SVM) with RBF and Polynomial kernels. We analyzed the effectiveness of the proposed system in performance parameters (accuracy (CA), sensitivity, specificity, and AUC). Compared to the results with existing BC diagnostic systems, the proposed method (Inception V3 + Staking) is superior, with performance parameters 0.947 of AUC and 0.858 of CA values. The proposed BCUI analysis system consists of data collection, pre-processing, transfer learning, ensemble stacking of ML models, and performance evaluation, with comparative analysis demonstrating its superiority over existing methods.

Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies

Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors. 

A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study

Abstract Purpose Explore the impact of pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) and changes in tumor blood supply on pathological complete response (pCR) in breast cancer(BC) patients following neoadjuvant chemotherapy (NACT). Additionally, establish and validate a nomogram for predicting pCR. Methods Included BC patients undergoing NACT at Yongchuan Hospital of Chongqing Medical University from January 2019 to October 2023. Analyzed the correlation between pCR and clinicopathological factors, as well as tumor ultrasound features, using chi-square or Fisher's exact test. Developed and validated a nomogram predicting pCR based on regression analysis results. Results The study included 176 BC patients, with 38.64% achieving pCR after NACT. Logistic regression analysis identified AAPR [odds ratio (OR): 2.616, 95% confidence interval (CI): 1.140–5.998, P = 0.023], changes in tumor blood supply after two NACT cycles (OR: 2.247, 95%CI: 1.071–4.716, P = 0.032), tumor histological grade (OR: 3.843, 95%CI: 1.286–10.659, P = 0.010), and HER2 status (OR: 2.776, 95%CI: 1.057–7.240, P = 0.038) as independent predictors of pCR after NACT. The nomogram, based on AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status, demonstrated a good predictive capability. Conclusion AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status independently predicted pCR in BC patients following NACT. The nomogram, based on these positive factors, demonstrated a good predictive ability. 

Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer by Clinicopathological Indicators and Ultrasound Parameters Using a Nomogram

Purpose To examine whether dynamic alterations in conventional ultrasound (US) and superb microvascular imaging (SMI) can act as predictors of pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) in breast cancer (BC). Methods This single-center, retrospective study included women with invasive BC who underwent NAC between January 2022 and December 2024. The features of conventional US and SMI characteristics of BC were analyzed before NAC and the change (Δ) after two cycles. Multivariate logistic regression analysis (Forward, Wald, α = 0.05) was used to screen factors independently associated with pCR. Area under the receiver operating characteristic curve (AUC) analysis was performed to confirm the predictive effectiveness and evaluate the internal validity through bootstrap resampling. A nomogram was created to graphically represent the predictive power of the various factors for pCR. Results Before NAC, the pCR group exhibited significantly higher negative rates for the estrogen receptor (ER) and progesterone receptor (PR). ( P &lt; 0.001 and P = 0.005, respectively) and significantly higher positive rates of human epidermal growth factor receptor 2 (HER2) and echogenic rinds ( P &lt; 0.001 and P = 0.029, respectively). Additionally, they exhibited significantly shorter largest diameters (LD) and shortest diameters (SD) ( P = 0.001 and P = 0.003). After two cycles of NAC, patients who achieved pCR exhibited a significantly higher proportion of monochrome superb microvascular imaging (mSMI) that had not expanded, as well as disappearance of the echogenic rind ( P &lt; 0.001 and P = 0.002). Regarding the rate of change in LD, SD, and vascular index (VI), patients in the pCR group showed significantly higher values than those in the non-pCR group (all P &lt; 0.001). The multivariate logistic regression model identified ΔVI (%), ΔSD (%), and SD to have the strongest association with pCR. The overall multivariate model demonstrated the best AUC (0.963), which was significantly higher than that of any single factor. Bootstrap resampling, calibration plots, and decision curve analysis (DCA) all demonstrated strong performance in both discrimination and calibration. Conclusion The baseline status of US and SMI, as well as the longitudinal changes, demonstrated good predictive performance for pCR in BC following NAC. 

Prediction of pathological complete response to neoadjuvant chemotherapy for invasive breast cancers based on longitudinal ultrasound and superb microvascular imaging: a single-center retrospective study

The study explored the impact of pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) and changes in tumor blood supply on pathological complete response (pCR) in breast cancer (BC) patients following neoadjuvant chemotherapy (NACT). Additionally, a nomogram for predicting pCR was established and validated. The study included BC patients undergoing NACT at Yongchuan Hospital of Chongqing Medical University from January 2019 to October 2023. We analyzed the correlation between pCR and clinicopathological factors, as well as tumor ultrasound features, using chi-square or Fisher's exact test. We developed and validated a nomogram predicting pCR based on regression analysis results. The study included 176 BC patients. Logistic regression analysis identified AAPR [odds ratio (OR) 2.616, 95% confidence interval (CI) 1.140-5.998, P = 0.023], changes in tumor blood supply after two NACT cycles (OR 2.247, 95%CI 1.071-4.716, P = 0.032), tumor histological grade (OR 3.843, 95%CI 1.286-10.659, P = 0.010), and HER2 status (OR 2.776, 95%CI 1.057-7.240, P = 0.038) as independent predictors of pCR after NACT. The nomogram, based on AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status, demonstrated a good predictive capability. 

Predicting pathological complete response after neoadjuvant chemotherapy in breast cancer by clinicopathological indicators and ultrasound parameters using a nomogram

Background The aim was to evaluate the causes of death for patients with localized, regional and metastatic penile cancer (PeCa) after diagnosis. Methods PeCa patients diagnosed during 2004-2018 in the Surveillance, Epidemiology, and End Results program database were identified. Causes of deaths including PeCa, second malignant tumors (SMTs) and non-tumor diseases were analyzed, as well as the standardized mortality ratio (SMR) of each cause. Results For localized PeCa, 800 of 2155 patients died during the follow-up. 24.9% of all deaths were due to PeCa. 18.0% and 57.1% deaths were due to SMTs and non-tumor causes. Main SMTs included cancers of lung and bronchus (n = 40) and skin (n = 11) with significantly increased SMRs of 1.71 (1.22-2.33) and 4.82 (2.41-8.63). Mortality risks of other SMTs were mostly similar with the general populations. Main causes of non-tumor diseases included diseases of heart [n = 172, SMR: 1.66 (1.42-1.93)], COPD and allied cond [n = 38, SMR: 1.63 (1.15-2.24)], and cerebrovascular diseases [n = 33, SMR: 1.71 (1.17-2.4)]. For regional PeCa, 679 of 1310 patients died including 43.5% PeCa, 14.8% SMTs and 26.6% non-tumor causes. The mortality risks of cancers from lung and bronchus [SMR: 2.41 (1.53-3.62)], skin [SMR: 6.41 (2.35-13.95)] and testis [SMR: 149.35 (18.09-539.5)] were significantly increased. Main non-tumor causes of death included diseases of heart [n = 71, SMR: 1.77 (1.38-2.23)], COPD and allied cond [n = 17, SMR: 1.85 (1.08-2.95)] and diabetes mellitus [n = 16, SMR: 3.62 (2.07-5.88)]. For distant diseases, 109 of 132 patients died including 76 (69.7%) died for PeCa itself, 24 (22.0%) died for SMTs and 9 (8.3%) died for non-tumor diseases. The majority of PeCa deaths (67.1%) and SMTs deaths (79.2%) occurred within 1 year after the diagnosis of PeCa. Conclusions We firstly analyzed the SMTs and non-tumor causes of death and morality risks of each cause for PeCa patients, which provided valuable information for PeCa patients on disease prevention and health care during their survivorship.

Second Malignant Tumors and Non-Tumor Causes of Death for Patients With Penile Cancer During Their Survivorship

<h2>Abstract</h2><h3>Objective</h3> The aim was to evaluate the causes of death for patients with testicular cancer (TC), and calculate mortality risks for each cause. <h3>Methods</h3> Patients diagnosed between 2000 and 2017 were identified. Main causes of death including TC, second malignant tumor (SMT) and non-tumor diseases, and the standardized mortality rate (SMR) of each cause were analyzed. <h3>Results</h3> 27,143 patients with localized TC were included, and 1171 of them died including 215 TC deaths, 236 SMT deaths, and 720 non-tumor deaths. Main SMT deaths were cancer from lung and bronchus, colon and rectum, etc. Main non-cancer causes were diseases of heart, accidents and adverse effects and suicide and self-inflicted injury. Compared with the general population, the mortality risks from diseases of heart and accidents and adverse effects were significantly reduced. For 11,719 patients with regional and distant metastasis TC, 1733 died including 964 TC deaths, 345 SMT deaths and 424 non-tumor deaths. The main SMT and non-tumor deaths were lung and bronchus, diseases of heart and suicide and self-inflicted injury. <h3>Conclusion</h3> The leading causes of death besides TC were lung and bronchus cancer, colon and rectum cancer, diseases of heart, accidents and adverse effects, suicide and self-inflicted injury for TC patients. The localized TC patients were associated with similar risks of SMT deaths and lower risks of main non-tumor causes of death. <h3>Impact</h3> We evaluated all causes of death of TC patients and SMR for each cause of death. Our results could provide valuable information about the priority of healthcare during testicular cancer survival. 

Causes of death among patients with testicular cancer during the survivorship.

To evaluate the second malignant tumors (SMTs) and non-tumor causes of death among patients diagnosed with localized and regional kidney cancer.Patients diagnosed with kidney cancer between 2000 and 2017 in the Surveillance, Epidemiology, and End Results (SEER) program database were identified. All causes of death for patients during the follow-up and standardized mortality ratio (SMR) were analyzed.113,734 patients with localized kidney cancer with 30,390 cases of death were analyzed. 60.4% of the death cases were due to non-tumor caused and 23.6% were second malignant tumors (SMTs). Main SMTs included cancers of lung and bronchus [n = 1,283, SMR: 1.00 (0.95-1.06)] and pancreas [n = 393, SMR: 1.27 (1.15-1.41)]. Causes of death for non-tumor mainly included heart diseases [n = 6,161, SMR: 1.25 (1.21-1.28)] and chronic obstructive pulmonary disease (COPD) [n = 1,185, SMR: 0.99 (0.94-1.05)]. 14,437 of 29,602 patients with regional kidney cancer died. 14.6% of all deaths were due to SMTs and 23.6% due to non-tumor causes. Main SMTs contained bladder cancer [n = 371, SMR: 10.90 (9.81-12.06)] and lung and bronchus cancer [n = 346, SMR: 1.21 (1.08-1.34)]. The main non-tumor death was heart disease [n = 1,424, SMR: 1.26 (1.2-1.33)]. When stratified by pathological types, patients with clear cell renal cell carcinoma (RCC) did not have increased mortality risks of bladder cancer and lung cancer but patients with non-clear cell RCC did.SMTs and non-tumor diseases including lung and bronchus cancer, bladder cancer, pancreas cancer, diseases of heart, COPD, and cerebrovascular diseases are the leading causes of death besides kidney cancer and should be paid more attention during patients' survival period. 

https://eurjmedres.biomedcentral.com/counter/pdf/10.1186/s40001-023-01176-6

Second malignant tumors and non-tumor causes of death for patients with localized and regional kidney cancer after diagnosis

Background The mortality rate of breast cancer (BC) ranks first among female tumors worldwide and presents a trend of younger age, which poses a great threat to women's health and life. Neoadjuvant chemotherapy (NAC) for breast cancer is defined as the first step of treatment for breast cancer patients without distant metastasis before planned surgical treatment or local treatment with surgery and radiotherapy. According to the current NCCN guidelines, patients with different molecular types of BC should receive neoadjuvant chemotherapy (NAC), which can not only achieve tumor downstaging, increase the chance of surgery, and improve the breast-conserving rate. In addition, it can identify new genetic pathways and drugs related to cancer, improve patient survival rate, and make new progress in breast cancer management. Objective To explore the role of the nomogram established by the combination of ultrasound parameters and clinical indicators in the degree of pathological remission of breast cancer. Methods A total of 147 breast cancer patients who received neoadjuvant chemotherapy and elective surgery in the Department of Ultrasound, Nantong Cancer Hospital, from May 2014 to August 2021 were retrospectively included. Postoperative pathological remission was divided into two groups according to Miller–Payne classification: no significant remission group (NMHR group, n = 93) and significant remission group (MHR group, n = 54). Clinical characteristics of patients were recorded and collected. The multivariate logistic regression model was used to screen the information features related to the MHR group, and then, a nomogram model was constructed; ROC curve area, consistency index (C-index, CI), calibration curve, and H-L test were used to evaluate the model. And the decision curve is used to compare the net income of the single model and composite model. Results Among 147 breast cancer patients, 54 (36.7%) had pathological remission. Multivariate logistic regression showed that ER, reduction/disappearance of strong echo halo, Adler classification after NAC, PR + CR, and morphological changes were independent risk factors for pathological remission (P < 0.05). Based on these factors, the nomogram was constructed and verified. The area under the curve (AUC) and CI were 0.966, the sensitivity and specificity were 96.15% and 92.31%, and the positive predictive value (PPV) and negative predictive value (NPV) were 87.72% and 97.15%, respectively. The mean absolute error of the agreement between the predicted value and the real value is 0.026, and the predicted risk is close to the actual risk. In the range of HRT of about 0.0∼0.9, the net benefit of the composite evaluation model is higher than that of the single model. H-L test results showed that χ2 = 8.430, P=0.393 > 0.05. Conclusion The nomogram model established by combining the changes of ultrasound parameters and clinical indicators is a practical and convenient prediction model, which has a certain value in predicting the degree of pathological remission after neoadjuvant chemotherapy.

/pdf/the-role-of-nomogram-based-on-the-combination-of-ultrasound-3t3jfuyg.pdf

The Role of Nomogram Based on the Combination of Ultrasound Parameters and Clinical Indicators in the Degree of Pathological Remission of Breast Cancer

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaotian Wu

Author Tools

Chat about Author

Papers

Second Malignant Tumors and Non-Tumor Causes of Death for Patients With Penile Cancer During Their Survivorship

Causes of death among patients with testicular cancer during the survivorship.

Second malignant tumors and non-tumor causes of death for patients with localized and regional kidney cancer after diagnosis

The Role of Nomogram Based on the Combination of Ultrasound Parameters and Clinical Indicators in the Degree of Pathological Remission of Breast Cancer