Browsing by Author "Zhang, M."

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    Personal history of rosacea and risk of incident cancer among women in the US
    (Nature Publishing Group, 2015-07-28) Li, W.-Q.; Zhang, M.; Danby, F. W.; Han, J.; Qureshi, A. A.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    BACKGROUND: Rosacea is an inflammatory skin disease. We examined the association between personal history of rosacea and risk of incident cancers. METHODS: A total of 75 088 whites were included from the Nurses' Health Study II (1991-2011). Information on clinician-diagnosed rosacea and diagnosis year was collected in 2005. All cancers other than basal cell carcinoma (BCC) were confirmed. RESULTS: During 1 447 205 person-years, we identified 5194 cases with internal malignancies and 5788 with skin cancers. We did not observe significant associations between personal history of rosacea and internal malignancies, except for thyroid cancer (hazard ratio (HR)=1.59, 95% confidence interval (CI)=1.07-2.36). Among skin cancers, personal history of rosacea was associated with an elevated risk of BCC (HR=1.50, 95% CI=1.35-1.67). CONCLUSIONS: We suggest possible associations between personal history of rosacea and an increased risk of thyroid cancer and BCC. Further studies are warranted to replicate our findings and to explore the underlying mechanisms.
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    Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma
    (American Society of Neuroradiology, 2021) Zhang, M.; Wong, S. W.; Lummus, S.; Han, M.; Radmanesh, A.; Ahmadian, S. S.; Prolo, L. M.; Lai, H.; Eghbal, A.; Oztekin, O.; Cheshier, S. H.; Fisher, P. G.; Ho, C. Y.; Vogel, H.; Vitanza, N. A.; Lober, R. M.; Grant, G. A.; Jaju, A.; Yeom, K. W.; Radiology and Imaging Sciences, School of Medicine
    Background and purpose: Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes. Materials and methods: We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features. Results: From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI. Conclusions: Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.
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    Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas
    (AJNR, 2022-04) Zhang, M.; Tam, L.; Wright, J.; Mohammadzadeh, M.; Han, M.; Chen, E.; Wagner, M.; Nemalka, J.; Lai, H.; Eghbal, A.; Ho, C. Y.; Lober, R. M.; Cheshier, S. H.; Vitanza, N. A.; Grant, G. A.; Prolo, L. M; Yeom, K. W.; Jaju, A.; Radiology and Imaging Sciences, School of Medicine
    BACKGROUND AND PURPOSE: Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging–based radiomics phenotypes that can differentiate these tumor types. MATERIALS AND METHODS: Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative–based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio. RESULTS: The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively. CONCLUSIONS: In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.