The Bland–Altman analysis: Does it have a role in assessing radiation dosimeter performance relative to an established standard?

Hill, Robin; Tofts, PS; Baldock, Clive

doi:10.1016/j.radmeas.2010.02.003

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…However, these results are at odds with a study by Aspradakis et al (Aspradakis and Zucchetti, 2015) which found PDD differences within ±5% for medium-energy beams and differences of more than 10% for beam energies between 50 kVp and 100 kVp. These results support the conclusions of Hill et al (Hill et al, 2010b) stating that discrepancies between measured PDDs and BJR 25 data could be attributed to differences in machines, detectors, phantoms, and measurement methods across various institutions. The results in this study were better matched with the published BJR 25 data.…”

Section: Discussionsupporting

confidence: 90%

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit

Xiong

Zhong

Banks

et al. 2022

Biomed. Phys. Eng. Express

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Objectives: The purpose of this study is to present data from the clinical commissioning of an Xstrahl 150 X-ray unit used for superficial radiotherapy. Methods: Commissioning tasks included vendor acceptance tests, timer reproducibility, linearity and end-effect measurements, half-value layer (HVL) measurements, inverse square law verification, head-leakage measurements, and beam output calibration. In addition, percent depth dose (PDD) curves were determined for different combinations of filter/kV settings and applicators. Automated PDD water phantom scans were performed utilizing four contemporary detectors: a microDiamond detector, a microSilicon detector, an EDGE detector, and a PinPoint ionization chamber. The measured PDD data were compared to the published values in BJR Supplement 25. Results: The x-ray unit’s mechanical, safety, and radiation characteristics were within vendor-stated specifications. Across sixty commissioned x-ray beams, the PDDs determined in water using solid state detectors were in excellent agreement with the BJR 25 data. For the lower (<100 kVp) and medium-energy (≥100 kVp) superficial beams the average agreement was within [-3.6,+0.4]% and [-3.7,+1.4]% range, respectively. For the high-energy superficial (low-energy orthovoltage) x-rays at 150 kVp, the average difference for the largest 20x20 cm2 collimator was (-0.7±1.0)%. Conclusions: This study presents machine characterization data collected for clinical use of a superficial x-ray unit. Special focus was placed on utilizing contemporary detectors and techniques for the relative PDD measurements using a motorized water phantom. The results in this study confirm that the aggregate values published in the BJR 25 report still serve as a valid benchmark when comparing data from site-specific measurements, or the reference data for clinical utilization without such measurements. Advances in knowledge: This paper presents comprehensive data from the acceptance and commissioning of a modern kilovoltage superﬁcial x-ray radiotherapy machine. Comparisons between the PDD data measured in this study using different detectors and BJR 25 data are highlighted.

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Section: Discussionsupporting

confidence: 90%

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit

Xiong

Zhong

Banks

et al. 2022

Biomed. Phys. Eng. Express

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“…After interpolation, their PDD comparison was within a 5% difference of the BJR 25 data. A similar study by Evans et al (14) obtained a percentage difference within 6.2% and, in a study by Hill et al, (36) the percentage difference was up to 12%. In a study focusing on measuring output factors in a kilovoltage therapy unit, (33) a percentage difference within 5% of the BJR 25's PDD data was obtained at one technique for four different HVL beams.…”

Section: Discussionmentioning

confidence: 59%

Modeling a superficial radiotherapy X‐ray source for relative dose calculations

Johnstone

LaFontaine

Poirier

et al. 2015

J Applied Clin Med Phys

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The purpose of this study was to empirically characterize and validate a kilovoltage (kV) X‐ray beam source model of a superficial X‐ray unit for relative dose calculations in water and assess the accuracy of the British Journal of Radiology Supplement 25 (BJR 25) percentage depth dose (PDD) data. We measured central axis PDDs and dose profiles using an Xstrahl 150 X‐ray system. We also compared the measured and calculated PDDs to those in the BJR 25. The Xstrahl source was modeled as an effective point source with varying spatial fluence and spectra. In‐air ionization chamber measurements were made along the x‐ and y‐axes of the X‐ray beam to derive the spatial fluence and half‐value layer (HVL) measurements were made to derive the spatially varying spectra. This beam characterization and resulting source model was used as input for our in‐house dose calculation software (kVDoseCalc) to compute radiation dose at points of interest (POIs). The PDDs and dose profiles were measured using 2, 5, and 15 cm cone sizes at 80, 120, 140, and 150 kVp energies in a scanning water phantom using IBA Farmer‐type ionization chambers of volumes 0.65 and 0.13 cc, respectively. The percent difference in the computed PDDs compared with our measurements range from −4.8% to 4.8%, with an overall mean percent difference and standard deviation of 1.5% and 0.7%, respectively. The percent difference between our PDD measurements and those from BJR 25 range from −14.0% to 15.7%, with an overall mean percent difference and standard deviation of 4.9% and 2.1%, respectively — showing that the measurements are in much better agreement with kVDoseCalc than BJR 25. The range in percent difference between kVDoseCalc and measurement for profiles was −5.9% to 5.9%, with an overall mean percent difference and standard deviation of 1.4% and 1.4%, respectively. The results demonstrate that our empirically based X‐ray source modeling approach for superficial X‐ray therapy can be used to accurately compute relative dose in a homogeneous water‐equivalent medium. They also show limitations in the accuracy of the BJR 25 PDD data.PACS number: 87.55.kh

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“…Kekuatannya terletak pada pemberian pinjaman dan mendapatkan jangka waktu pengembalian hutang yang tergantung kepercayaan kreditor terhadap kelikuiditasan suatu bank atau lembaga keuangan lainnya. (Hill et al, 2010) 3) Faktor pesaing Faktor ini hal yang harus diperhatikan karena menyangkut perbedaan pemberian pelayanan kepada nasabah, perusahaan juga jangan melupakan pesaingnya karena jika produk pesaingnya lebih diterima oleh masyarakat perusahaan tersebut akan kehilangan konsumen dan mengurangi pendapatan yang diterima. c. Faktor internal perusahaan faktor-faktor yang menyebabkan kebangkrutan secara internal menurut (Sulaiman et al, 2019) sebagai berikut : 1) terlalu besarnya kredit yang diberikan kepada konsumen sehingga akan menyebabkan adanya penunggakan dalam pembayaran sampai akhirnya tidak dapat membayar.…”

Section: ) Faktor Pemasok / Krediturunclassified

Penggunaan Springate Model Sebagai Prediktor Kebangkrutan Perusahaan Di Bursa Efek Indonesia

Abadi

2021

JEBAKU

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This study aims to determine and analyze the calculation of the Springate model in the property industry and predict which companies are in the category of potential bankruptcy. the variables used in this study are the Springate S-Score method, financial ratios and bankruptcy score results. The samples used were 6 property companies listed on the Indonesia Stock Exchange from the 2019-2021 period. This study uses the Springate S-Score analysis method which is practiced with liquidity, leverage, profitability and activity ratios. The results showed that the Springate model can be used as a predictor of corporate bankruptcy, the next result shows that bankruptcy analysis using the Springate method in the property industry shows the results that there are three companies that are declared healthy, namely PT Ciputra Surya in 2019, PT Indonesia Prima Property and PT Duta Pertiwi in 2021 with sequential S-Score values of 1.313, 0.935 and 0.867, the rest are declared as companies that have the potential for bankruptcy. The third result shows that the Working capital to total assets ratio, Net profit before interest and taxes to total assets, Net profit before interest and taxes to current liability and Sales to total assets ratio play a significant role in determining the results of the bankruptcy score in the property industry.

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The Bland–Altman analysis: Does it have a role in assessing radiation dosimeter performance relative to an established standard?

Cited by 12 publications

References 31 publications

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit

Modeling a superficial radiotherapy X‐ray source for relative dose calculations

Penggunaan Springate Model Sebagai Prediktor Kebangkrutan Perusahaan Di Bursa Efek Indonesia

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