Intravascular large B-cell lymphoma: a chameleon with multiple faces and many masks.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications.

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma

BACKGROUND To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions. METHODS A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self-verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out-of-group verification was performed by k-fold cross-validation. RESULTS There were statistically significant differences in age, maximum standardized uptake value (SUVmax ), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e-x ), x = - 5.583 + 0.039 × age + 0.477 × SUVmax  + 0.139 × size + 1.537 × lobulation - 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883-0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K-fold cross-validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053. CONCLUSIONS The risk factors for malignant nodules included age, SUVmax , size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.

A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating.

Kimura's disease is a clinically rare, chronic, benign lymphoproliferative disorder of unknown etiology. A 36-year-old man presented with painless axillary swelling, which was suspected as lymphoma. PET/CT was performed for staging. The images showed multiple foci of increased activity in bilateral axillary and right inguinal lymph nodes. Laboratory tests revealed an increased eosinophil ratio and eosinophil count. Pathological examination from dissected axillary lymph nodes was consistent with Kimura's disease.

Lymphadenopathy Due to Kimura's Disease Mimicking Lymphoma on FDG PET/CT.

Synthesis and Evaluation of Radioiodine-Labeled pH (Low) Insertion Peptide Variant 7-Like Peptide as a Noninvasive Tumor Microenvironment Imaging Agent in a Mouse MDA-MB-231 Triple-Negative Breast Cancer Model

ABSTRACT
A 66-year-old woman having intermittent abdominal distention for 2 months was referred to our hospital. An enhanced abdominal CT scan showed multiple hepatic lesions, suspected of metastases. An 18F-FDG PET/CT scan, which was performed to seek primary tumor, showed multiple lesions with intense 18F-FDG uptake in the liver and lymph nodes located in cardiophrenic angle, abdominal cavity, and retroperitoneal space. All 18F-FDG-avid lesions were suspected of metastases. However, no suspected primary tumor was identified by the 18F-FDG PET/CT scan, which prompted the liver biopsy. Pathologic analysis revealed the presence of plasmacytoma.

Extramedullary Plasmacytoma: Hepatic Lesions and Enlarged Lymph Nodes With Intense 18F-FDG Uptake Mimicking Metastases.

Abstract Background The aim of this study was to prepare a novel 68 Ga-labeled pH (low) insertion peptide (pHLIP)-like peptide, YJL-4, and determine its value for the early diagnosis of triple-negative breast cancer (TNBC) via in vivo imaging of tumor-bearing nude mice. The novel peptide YJL-4 was designed using a template-assisted method and synthesized by solid-phase peptide synthesis. After modification with the chelator 1,4,7‑triazacyclononane-N,N′,N″-triacetic acid (NOTA), the peptide was labeled with 68 Ga. Then, the biodistribution of 68 Ga-YJL-4 in tumor-bearing nude mice was investigated, and the mice were imaged by small animal positron emission tomography (PET). Results The radiochemical yield and radiochemical purity of 68 Ga-YJL-4 were 89.5 ± 0.16% and 97.95 ± 0.06%, respectively. The biodistribution of 68 Ga-YJL-4 in tumors (5.94 ± 1.27% ID/g, 6.72 ± 1.69% ID/g and 4.54 ± 0.58% ID/g at 1, 2 and 4 h after injection, respectively) was significantly greater than that of the control peptide in tumors at the corresponding time points ( P &lt; 0.01). Of the measured off-target organs, 68 Ga-YJL-4 was highly distributed in the liver and blood. The small animal PET imaging results were consistent with the biodistribution results. The tumors were visualized by PET at 2 and 4 h after the injection of 68 Ga-YJL-4. No tumors were observed in the control group. Conclusions The novel pHLIP family peptide YJL-4 can adopt an α- helical structure for easy insertion into the cell membrane in an acidic environment. 68 Ga-YJL-4 was produced in high radiochemical yield with good stability and can target TNBC tissue. Moreover, the strong concentration of radioactive 68 Ga-YJL-4 in the abdomen does not hinder the imaging of early TNBC. 

Small animal PET imaging with the 68Ga-labeled pH (low) insertion peptide-like peptide YJL-4 in a triple-negative breast cancer mouse model.

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.

Mingming Yu

Author Tools

Chat about Author