ArticlePhysics in medicine and biology2018
Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker.
Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Human biomarker navigator.iMeta · 2026Review
- Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions.Cancers · 2026Review
- A vision transformer-radiomics approach for enhanced chemotherapy outcome prediction in ovarian cancer.Frontiers in radiology · 2026Article
- Review
- The Role of Circulating Tumor DNA in Ovarian Cancer.Cancers · 2024Review
- CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors.Journal of imaging informatics in medicine · 2024Article
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Article
- Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.Journal of X-ray science and technology · 2022Article
- COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.Scientific reports · 2021Article
- Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images.Chaos, solitons, and fractals · 2021Article
- Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review.Cancers · 2021Review
- Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms.Journal of oncology · 2021Review
- Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients.Computer methods and programs in biomedicine · 2020Article
- Prediction of the treatment response in ovarian cancer: a ctDNA approach.Journal of ovarian research · 2020Review
- COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images.medRxiv : the preprint server for health sciences · 2020Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
- Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.Computer methods and programs in biomedicine · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
This study aimed to investigate the feasibility of integrating image features computed from both spatial and frequency domain to better describe the tumor heterogeneity for precise prediction of tumor response to postsurgical chemotherapy in patients with advanced-stage ovarian cancer. A computer-aided scheme was applied to first compute 133 features from five categories namely, shape and density, fast Fourier transform, discrete cosine transform (DCT), wavelet, and gray level difference method. An optimal feature cluster was then determined by the scheme using the particle swarm optimization algorithm aiming to achieve an enhanced discrimination power that was unattainable with the single features. The scheme was tested using a balanced dataset (responders and non-responders defined using 6 month PFS) retrospectively collected from 120 ovarian cancer patients. By evaluating the performance of the individual features among the five categories, the DCT features achieved the highest predicting accuracy than the features in other groups. By comparison, a quantitative image marker generated from the optimal feature cluster yielded the area under ROC curve (AUC) of 0.86, while the top performing single feature only had an AUC of 0.74. Furthermore, it was observed that the features computed from the frequency domain were as important as those computed from the spatial domain. In conclusion, this study demonstrates the potential of our proposed new quantitative image marker fused with the features computed from both spatial and frequency domain for a reliable prediction of tumor response to postsurgical chemotherapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.