Search

Objective: To evaluate digital voice recognition in free text mode., Study Design: A standard medical text (182 words and punctuation marks) was dictated four times each by six people using a commercially available voice recognition system., Results: The time used, including the time needed for making corrections, decreased from a median of 14.5 (6.3-18.8) minutes in the first session to a median of 6.7 (5.1-15.7) in the fourth session. the number of corrections necessary in the last session was a median of 20.5 (15-97)-i.e., 11% of the number of words in the text. All users described working with the system as highly strenuous. Dictating the same text with a dictaphone took median 1.4 (0.9-1.6) minutes and about five minutes of typing and correction time., Conclusion: Although the total time was about the same with the two methods, the time for the pathologist increased fourfold when using the voice recognition system. To obtain acceptable input speed, an appropriate template module is essential.

The usefulness of quantitative nuclear image features (QNI) for the histological classification of lung carcinomas was investigated. As no clear distinction could be established between the distributions of these features for the nuclei of squamous cell, adenocarcinoma, and large cell carcinoma, the attention was restricted to the discrimination between small cell lung carcinoma (SCLC) and non-small cell carcinoma (NSCLC). This discrimination is the crucial one in discussions about the choice of treatment. The differences between SCLC and NSCLC are statistically highly significant for various QNI features. The use of more than one QNI feature hardly raised the discriminatory performance with respect to the distinction between SCLC and NSCLC. Inferences were made about the probability and confidence interval of SCLC for a given QNI feature. It is concluded that in cases of uncertainty or disagreement, nuclear characteristics are useful for the discrimination between SCLC and NSCLC.

A feasibility study showed that quantitative nuclear image (QNI) analysis, in which the morphology of the nucleus is described by a number of mathematical parameters, can be used to make the therapeutically and prognostically important distinction between small cell lung carcinoma (SCLC) and non-SCLC, which can be difficult to make with subjective histologic typing. In the present study, the effects of sample size and sample site on the QNI features were investigated. For all sample sites in a given tumor, comparison was made between the histologic classification, the ultrastructural findings and the classification based on the QNI features. Using a running mean, it was found that a sample size of 25 nuclei is sufficiently large. Histologic and quantitative classifications of samples from different sites of the same tumors were in agreement with regard to the separation of SCLC and non-SCLC in 19 of 20 sections. In the case in which disagreement occurred in one section, the ultrastructural findings supported the quantitative classification. These data indicate that sampling from different sites has no essential influence on the QNI classification of lung carcinomas.

By means of a special apparatus a superficial lesion was made in the common bile duct of the rabbit; the healing of the lesion was studied by transmitted light microscopy and scanning electron microscopy. Within a few hours the cells in the epithelial margin became flattened and started to migrate over the bottom of the defect. In most cases the lesion was closed after 16 to 24 hours by epithelial migration. After 24 hours a sharp increase in the mitotic activity occurred, first especially in the crypts, later also in the superficial epithelium. A blood clot on or necrosis of the wound basis seemed to stop the migration of the epithelium. Necrosis of the connective tissue in the central part of the lesion occurred after 24 hours if the lesion was not epithelialized, possibly by the action of bile. In that case the lesion healed by secondary intention. After 2-8 weeks all lesions were healed completely, no strictures being observed.

Seventy-eight patients were investigated by magnetic resonance (MR) imaging using optimal scan parameters and a surface coil. Forty-two patients were also examined by computer tomography (CT). Sixteen patients underwent laryngectomy. MR imaging of cancerous tissue in the larynx, and particularly of non-invaded and invaded cartilages, was examined by comparing MR images with sliced surgical specimens. Pre-operative CT and MRI findings were evaluated by comparing them with postoperative histopathological findings. MR T1-weighted images demonstrate localisation and extent of cancerous tissue. With combined use of T1-weighted and proton-density images MR imaging is superior to CT for showing cartilage invasion. Unfortunately, gross movement artifacts, which resulted in non-diagnostic images, occurred in 16% of the examinations.