Cancer Imaging

Papers
(The H4-Index of Cancer Imaging is 20. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-09-01 to 2025-09-01.)
ArticleCitations
Predicting first-line VEGFR-TKI resistance and survival in metastatic clear cell renal cell carcinoma using a clinical-radiomic nomogram55
Optimisation of low and ultra-low dose scanning protocols for ultra-extended field of view PET in a real-world clinical setting53
Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT35
Comparison of the diagnostic accuracy of enhanced-CT and double contrast-enhanced ultrasound for preoperative T-staging of gastric cancer: a meta-analysis31
Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma29
Whole-tumoral metabolic heterogeneity in 18F-FDG PET/CT is a novel prognostic marker for neuroblastoma28
Intravoxel incoherent motion imaging combined with diffusion kurtosis imaging to assess the response to radiotherapy in a rabbit VX2 malignant bone tumor model26
Prediction of lateral lymph node metastasis with short diameter less than 8 mm in papillary thyroid carcinoma based on radiomics25
An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma24
A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI24
A whole-body diffusion MRI normal atlas: development, evaluation and initial use22
Clinical application of machine learning models in patients with prostate cancer before prostatectomy22
Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines21
Evaluation of 3D ARFI imaging of prostate cancer: diagnostic reliability and concordance with MpMRI21
Glymphatic system dysfunction and cerebrospinal fluid retention in gliomas: evidence from perivascular space diffusion and volumetric analysis21
Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients21
Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment21
The prognostic significance of semi-quantitative metabolic parameters and tumoral metabolic activity based on 123I-MIBG SPECT/CT in pretreatment neuroblastoma patients20
Diagnostic performance and prognostic value of preoperative 18F-FDG PET/CT in renal cell carcinoma patients with venous tumor thrombus20
Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment20
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