Frontiers in oncology
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Frontiers in oncology · Jan 2020
Survival Following Segmentectomy or Lobectomy in Patients With Stage IB Non-small-cell Lung Cancer.
Background: Lobectomy with mediastinal lymph node dissection has always been recognized as the standardized treatment for early-stage non-small-cell lung cancer. However, the feasibility of segmentectomy performed in stage IB non-small-cell lung cancer (NSCLC) patients remains controversial. The present study aims to investigate whether the outcome of stage IB NSCLC patients undergoing segmentectomy was comparable to those who underwent lobectomy. ⋯ Conclusion: Segmentectomy achieved equivalent OS and LCSS in stage IB NSCLC patients with TS ≤ 30 mm compared with lobectomy. Lobectomy showed better OS and LCSS than segmentectomy for patients with a TS of >30 and ≤ 40 mm. Segmentectomy may be acceptable in patients with an older age and a smaller TS.
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Frontiers in oncology · Jan 2020
Treating Hematologic Malignancies During a Pandemic: Utilizing Telehealth and Digital Technology to Optimize Care.
In late January 2020, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2) was reported as an outbreak in Wuhan, China. Within 2 months it became a global pandemic. Patients with cancer are at highest risk for both contracting and suffering complications of its resultant disease, Coronavirus 19 (COVID-19). ⋯ Bringing care to the home through the use of telehealth, home based chemotherapy, and remote patient monitoring technologies can help minimize risk to the patient and healthcare workers without sacrificing quality of care delivered. These care models provide the right treatment, to the right patient, at the right time, in the right place. Whether these patient-centered models of care will continue to be embraced by key stakeholders after the pandemic remains uncertain.
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Frontiers in oncology · Jan 2020
Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma.
Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery. ⋯ We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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Frontiers in oncology · Jan 2020
Stereotactic Cavity Irradiation or Whole-Brain Radiotherapy Following Brain Metastases Resection-Outcome, Prognostic Factors, and Recurrence Patterns.
Introduction: Following the resection of brain metastases (BM), whole-brain radiotherapy (WBRT) is a long-established standard of care. Its position was recently challenged by the less toxic single-session radiosurgery (SRS) or fractionated stereotactic radiotherapy (FSRT) of the resection cavity, reducing dose exposure of the healthy brain. Patients and Methods: We analyzed 101 patients treated with either SRS/FSRT (n = 50) or WBRT (n = 51) following BM resection over a 5-year period. ⋯ Conclusion: This is the first propensity score-adjusted direct comparison of SRS/FSRT and WBRT following the resection of BM. Patients receiving SRS/FSRT showed longer OS and LC compared to WBRT. Future analyses will address the optimal choice of safety margin, dose and fractionation for postoperative stereotactic RT of the resection cavity.
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Frontiers in oncology · Jan 2020
Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer.
Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. ⋯ Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.