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Background: Th e effi cacy and reproducibility of mammographic tumour feature use for predicting patient outcome were tested in consecutive in situ and 1-14 mm invasive breast cancer cases from two breast centres in two diff erent health care systems.Methods: All in situ and 1-14 mm invasive cancers detected in Falun from 1996-2006 (n = 971), in Roanoke from 2002-2007 (n = 555), and in women aged 40-69 (age limits for invitation to screening) in Falun from 1977-1995 (n = 844) were included; of these the mammograms, pathology slides and follow-up information were available in 95%, 97% and 91% of the cases, respectively. Th e cancers were classifi ed according to their mammographic appearance: stellate or circular without associated calcifi cations, or malignant type calcifi cations with or without an associated tumour mass. Th e mammographic tumour features and the disease specifi c survival were correlated. Terminal digit preference of tumour size measurements was examined.Results: Mammographic tumour features were similarly represented in both centres. A signifi cant preference was observed for tumour size measurements divisible by 5 mm. Outcome was signifi cantly poorer for cases having casting type calcifi cations on the mammogram and excellent for the remaining cases.Conclusions: Outcome prediction of patients with 1-14 mm invasive breast cancer is signifi cantly improved by the addition of mammographic tumour features to the currently used prognostic factors. Th e integration of imaging morphology into the TNM classifi cation of invasive breast cancers smaller than 15 mm facilitates specifi cally targeted therapy and may curtail overtreatment. Th e signifi cant digit preference found in this study may justify using the terminal digits of "4" and/or "9" as upper size limits for tumour size categories.

Our aim was to determine the effect of wearing a surgical mask on the number and type of dictation errors in unedited radiology reports. IRB review was waived for this prospective matched-pairs study in which no patient data was used. Model radiology reports ( n = 40) simulated those typical for an academic medical center. Six randomized radiologists dictated using speech-recognition software with and without a surgical mask. Dictations were compared to model reports and errors were classified according to type and severity. A statistical model was used to demonstrate that error rates for all types of errors were greater when masks are worn compared to when they are not (unmasked: 21.7 ± 4.9 errors per 1000 words, masked: 27.1 ± 2.2 errors per 1000 words; adjusted p < 0.0001). A sensitivity analysis was performed, excluding a reader with a large number of errors. The sensitivity analysis found a similar difference in error rates for all types of errors, although significance was attenuated (unmasked: 16.9 ± 1.9 errors per 1000 words, masked: 20.1 ± 2.2 errors per 1000 words; adjusted p = 0.054). We conclude that wearing a mask results in a near-significant increase in the rate of dictation errors in unedited radiology reports created with speech-recognition, although this difference may be accentuated in some groups of radiologists. Additionally, we find that most errors are minor single incorrect words and are unlikely to result in a medically relevant misunderstanding.

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