Survival impact of waiting time for radical radiotherapy in nasopharyngeal carcinoma: A large institution-based cohort study from an endemic area

Hu, Liang; Xiang, Yan; Lv, Xing; Xie, Chang; Cao, Su Mei; Wang, Lin; Qian, Chao Nan; Yang, Jing; Ye, Yan Fang; Guo, Feng; Ke, Liang Ru; Yu, Ya Hui; Liu, Guo Ying; Qiu, Wen‐Ze; Huang, Xin; Wen, Can; You, Na; Wang, Xue Qin; Guo, Xiang; Xia, Wei

doi:10.1016/j.ejca.2016.12.009

Cited by 37 publications

(35 citation statements)

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“…These have reported increasing mortality and advanced stage of disease with longer diagnostic intervals for breast cancer (Ermiah et al, 2012;Jensen, Madsen, & Overgaard, 2008;Richards, Westcombe, Love, Littlejohns, & Ramirez, 1999;Warner et al, 2012), colorectal cancer (Corley et al, 2017;Tørring et al, 2012Tørring et al, , 2011, head and neck cancers (Chen, King, Pearcey, Kerba, & Mackillop, 2008;Liang et al, 2017;Murphy et al, 2016), endometrial cancer (Dolly et al, 2016) and lung cancer (Jensen, Mainz, & Overgaard, 2002;Olsson, Schultz, & Gould, 2009;Wang, Mahasittiwat, Wong, Quint, & Kong, 2012). These have reported increasing mortality and advanced stage of disease with longer diagnostic intervals for breast cancer (Ermiah et al, 2012;Jensen, Madsen, & Overgaard, 2008;Richards, Westcombe, Love, Littlejohns, & Ramirez, 1999;Warner et al, 2012), colorectal cancer (Corley et al, 2017;Tørring et al, 2012Tørring et al, , 2011, head and neck cancers (Chen, King, Pearcey, Kerba, & Mackillop, 2008;Liang et al, 2017;Murphy et al, 2016), endometrial cancer (Dolly et al, 2016) and lung cancer (Jensen, Mainz, & Overgaard, 2002;Olsson, Schultz, & Gould, 2009;Wang, Mahasittiwat, Wong, Quint, & Kong, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Although the benefits of expedited diagnosis of symptomatic cancer cannot be experimentally tested, some evidence exists from observational studies. These have reported increasing mortality and advanced stage of disease with longer diagnostic intervals for breast cancer (Ermiah et al, 2012;Jensen, Madsen, & Overgaard, 2008;Richards, Westcombe, Love, Littlejohns, & Ramirez, 1999;Warner et al, 2012), colorectal cancer (Corley et al, 2017;Tørring et al, 2012Tørring et al, , 2011, head and neck cancers (Chen, King, Pearcey, Kerba, & Mackillop, 2008;Liang et al, 2017;Murphy et al, 2016), endometrial cancer (Dolly et al, 2016) and lung cancer (Jensen, Mainz, & Overgaard, 2002;Olsson, Schultz, & Gould, 2009;Wang, Mahasittiwat, Wong, Quint, & Kong, 2012). Nevertheless, most of these findings need to be replicated using analytical methods that address many of the problems associated with previous studies of this nature (Neal et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Advanced‐stage cancer and time to diagnosis: An International Cancer Benchmarking Partnership (ICBP) cross‐sectional study

Tørring

Falborg²,

Jensen³

et al. 2019

Eur J Cancer Care

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Objective: To investigate the relationship between tumour stage at diagnosis and selected components of primary and secondary care in the diagnostic interval for breast, colorectal, lung and ovarian cancers. Methods:Observational study based on data from 6,162 newly diagnosed symptomatic cancer patients from Module 4 of the International Cancer Benchmarking Partnership. We analysed the odds of advanced stage of cancer as a flexible function of the length of primary care interval (days from first presentation to referral) and secondary care interval (days from referral to diagnosis), respectively, using logistic regression with restricted cubic splines. Results:The association between time intervals and stage was similar for each type of cancer. A statistically significant U-shaped association was seen between the secondary care interval and the diagnosis of advanced rather than localised cancer, odds decreasing from the first day onwards and increasing around three and a half months.A different pattern was seen for the primary care interval, flat trends for colorectal and lung cancers and a slightly curved association for ovarian cancer, although not statistically significant. Conclusion:The results confirm previous findings that some cancers may progress even within the relatively short time frame of regulated diagnostic intervals. The study supports the current emphasis on expediting symptomatic diagnosis of cancer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced‐stage cancer and time to diagnosis: An International Cancer Benchmarking Partnership (ICBP) cross‐sectional study

Tørring

Falborg²,

Jensen³

et al. 2019

Eur J Cancer Care

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“…For elective priority treatments, i.e. head and neck cancers, rectal and anal cancer, gastroesophageal junction, we select patients to avoid delays in consultation and treatment which may adversely affect potentially curable cancer patients [7].…”

Section: Non-urgent Treatmentsmentioning

confidence: 99%

Radiotherapy during COVID-19 pandemic. How to create a No fly zone: a Northern Italy experience

et al. 2020

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Background SARS-CoV-2 pandemic represents a troubling health emergency but also a main challenge for the clinical governance of the system. Discontinuation of radiation treatments is not desirable and potentially life-threatening. On the other hand, accesses to hospital expose cancer patients to an increased risk of COVID-19 infection. We report our extended protocol, draft to manage clinical activities in our radiotherapy department, by minimizing contagion risks. Methods We used telephonic screening to assess the need for patient admission. A telephonic triage was performed to identify the presence of COVID-19 infection risk factors or symptoms. New treatments were stratified according to priority codes. A reserved entrance to radiotherapy department was assured for patients and staff. Surgical disposable mask was required for patients and caregivers. The activities were distributed during the whole workday, avoiding overlap to reduce aggregation. Results From 1st February 2020 to 31 March 2020, we reported an increase in the number of first medical examinations and treatments, compared to the same period of the previous year. Outpatients first medical examinations have been spread over the 12 working hours. No COVID-19 cases were detected. Conclusion During COVID-19 pandemic, we introduced procedures that allowed us to ensure the continuity in oncological cares, with limited risks of infection for patients and staff.

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Section: Introductionmentioning

confidence: 99%

“…The majority of NPC patients are diagnosed at an advanced stage, contributing to the dismal prognosis of the disease [ 4 ]. The 10-year overall survival (OS) is 50%–70% for late stage NPC patients [ 4 – 6 ]. Given the rapid development of imaging techniques and radiotherapy, the local control rate of early NPC patients has increased up to 95% [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an endoscopic images‐based deep learning model for detection with nasopharyngeal malignancies

Jing

et al. 2018

Cancer Communications

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BackgroundDue to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.MethodsAn endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts.ResultsAll images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively.ConclusionsThe eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.

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Survival impact of waiting time for radical radiotherapy in nasopharyngeal carcinoma: A large institution-based cohort study from an endemic area

Cited by 37 publications

References 32 publications

Advanced‐stage cancer and time to diagnosis: An International Cancer Benchmarking Partnership (ICBP) cross‐sectional study

Advanced‐stage cancer and time to diagnosis: An International Cancer Benchmarking Partnership (ICBP) cross‐sectional study

Radiotherapy during COVID-19 pandemic. How to create a No fly zone: a Northern Italy experience

Development and validation of an endoscopic images‐based deep learning model for detection with nasopharyngeal malignancies

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