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  • 1
    Online Resource
    Online Resource
    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-10-7)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-10-7)
    Abstract: We aimed to evaluate the efficacy and feasibility of concurrent weekly docetaxel-nedaplatin and hypo-fractionated radiotherapy (hypo-RT) in atypical histologic subtypes of primary and metastatic mediastinal malignancies. Methods Fifty-four patients diagnosed with atypical primary or metastatic mediastinal malignancies were retrospectively reviewed. 30 patients received concurrent weekly docetaxel and nedaplatin and hypo-RT (CChRT group) and 24 patients had hypo-RT alone (hRT group). Overall response rate (ORR), in-field locoregional progression-free survival (LPFS) and toxicities were analyzed. The radiobiological effect was evaluated by the LQRGC/TCP model, incorporating four “R”s of radiobiology, Gompertzian tumor growth and radio-sensitizing effect of chemotherapeutic agent. The biologically effective doses (BEDs) were calculated. Results The median follow-up time was 29.2 months for all patients. The ORR was 86.7% in CChRT group, compared with 62.5% in hRT group ( p =0.033). The 2-year in-field LPFS of CChRT and hRT group was 73.4% and 47.3%, respectively ( p =0.003). There was no significant difference of any & gt;=Grade 3 toxicities between the two groups ( p =0.754). The mean total dose and mean BED by the LQRGC/TCP model in CChRT group were 58.2Gy and 72.34Gy, versus 52.6Gy and 67.25Gy in hRT group. Conclusions Concurrent weekly docetaxel-nedaplatin and hypo-RT achieved promising in-field LPFS and tolerable toxicities compared with hypo-RT alone in different histologic subtypes of primary and metastatic mediastinal malignancies.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.974394
    DOI: 10.3389/fonc.2022.974394.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 2
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    Image_2.eps
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-6-6)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-6-6)
    Abstract: A significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM. Methods The receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM. Results Discordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set. Conclusions Receptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.878388
    DOI: 10.3389/fonc.2022.878388.s001
    DOI: 10.3389/fonc.2022.878388.s002
    DOI: 10.3389/fonc.2022.878388.s003
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 3
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    Online Resource
    Decreased Pituitary Height and Stunted Linear Growth After Radiotherapy in Survivors of Childhood Nasopharyngeal Carcinoma Cases
    Frontiers Media SA ; 2018
    In:  Frontiers in Endocrinology Vol. 9 ( 2018-11-19)
    Details
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 9 ( 2018-11-19)
    Type of Medium: Online Resource
    ISSN: 1664-2392
    URL: Article
    DOI: 10.3389/fendo.2018.00643
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2018
    detail.hit.zdb_id: 2592084-4
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  • 4
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-7-14)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-7-14)
    Abstract: Lung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor ( EGFR ) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases. Methods Data from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR , 92 with wild-type EGFR ) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set. Results The best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively. Conclusions Radiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    URL: Article
    DOI: 10.3389/fonc.2022.931812
    DOI: 10.3389/fonc.2022.931812.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2649216-7
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  • 5
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    Online Resource
    Evaluating Heterogeneity of Primary Lung Tumor Using Clinical Routine Magnetic Resonance Imaging and a Tumor Heterogeneity Index
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 10 ( 2021-1-8)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 10 ( 2021-1-8)
    Abstract: To improve the assessment of primary tumor heterogeneity in magnetic resonance imaging (MRI) of non-small cell lung cancer (NSCLC), we proposed a method using basic measurements from T1- and T2-weighted MRI. Methods One hundred and four NSCLC patients with different T stages were studied. Fifty-two patients were analyzed as training group and another 52 as testing group. The ratios of standard deviation (SD)/mean signal value of primary tumor from T1-weighted (T1WI), T1-enhanced (T1C), T2-weighted (T2WI), and T2 fat suppression (T2fs) images were calculated. In the training group, correlation analyses were performed between the ratios and T stages. Then an ordinal regression model was built to generate the tumor heterogeneous index (THI) for evaluating the heterogeneity of tumor. The model was validated in the testing group. Results There were 11, 32, 40, and 21 patients with T1, T2, T3, and T4 disease, respectively. In the training group, the median SD/mean on T1WI, T1C, T2WI, and T2fs sequences was 0.11, 0.19, 0.16, and 0.15 respectively. The SD/mean on T1C (p=0.003), T2WI (p=0.000), and T2fs sequences (p=0.002) correlated significantly with T stages. Patients with more advanced T stage showed higher SD/mean on T2-weighted, T2fs, and T1C sequences. The median THI in the training group was 2.15. THI correlated with T stage significantly (p=0.000). In the testing group, THI was also significantly related to T stages (p=0.001). Higher THI had relevance to more advanced T stage. Conclusions The proposed ratio measurements and THI based on MRI can serve as functional radiomic markers that correlated with T stages for evaluating heterogeneity of lung tumors.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2020.591485
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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