Operative Treatment of Acute Fractures of the Tarsal Navicular Body: Midterm Results With a New Classification.

BACKGROUND Treatment of displaced tarsal navicular body fractures usually consists of open reduction and internal fixation. However, there is little literature reporting results of this treatment and correlation to fracture severity. METHODS We report the results of 24 patients treated in our institution over a 12-year period. Primary outcome measurements were Visual-Analogue-Scale Foot and Ankle score (VAS-FA), AOFAS midfoot score, and talonavicular osteoarthritis at final follow-up. According to a new classification system reflecting talonavicular joint damage, 2-part fractures were classified as type I, multifragmentary fractures as type II, and fractures with talonavicular joint dislocation and/or concomitant talar head fractures as type III. Spearman's coefficients tested this classification's correlation with the primary outcome measurements. Mean patient age was 33 (range 16-61) years and mean follow-up duration 73 (range 24-159) months. RESULTS Average VAS-FA score was 74.7 (standard deviation [SD] 16.9), and average AOFAS midfoot score was 83.8 (SD = 12.8). Final radiographs showed no talonavicular arthritis in 5 patients, grade 1 in 7, grade 2 in 3, grade 3 in 6, and grade 4 in 1 patient. Two patients had secondary or spontaneous talonavicular fusion. Spearman coefficients showed strong correlation of the classification system with VAS-FA score (r = -0.663, P < .005) and talonavicular arthritis (r = 0.600, P = .003), and moderate correlation with AOFAS score (r = -.509, P = .011). CONCLUSION At midterm follow-up, open reduction and internal fixation of navicular body fractures led to good clinical outcome but was closely related to fracture severity. A new classification based on the degree of talonavicular joint damage showed close correlation to clinical and radiologic outcome. LEVEL OF EVIDENCE Level IV, retrospective case series.

The comprehensiveness of the ESHRE/ESGE classification of female genital tract congenital anomalies: a systematic review of cases not classified by the AFS system.

STUDY QUESTION How comprehensive is the recently published European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) classification system of female genital anomalies? SUMMARY ANSWER The ESHRE/ESGE classification provides a comprehensive description and categorization of almost all of the currently known anomalies that could not be classified properly with the American Fertility Society (AFS) system. WHAT IS KNOWN ALREADY Until now, the more accepted classification system, namely that of the AFS, is associated with serious limitations in effective categorization of female genital anomalies. Many cases published in the literature could not be properly classified using the AFS system, yet a clear and accurate classification is a prerequisite for treatment. STUDY DESIGN, SIZE AND DURATION The CONUTA (CONgenital UTerine Anomalies) ESHRE/ESGE group conducted a systematic review of the literature to examine if those types of anomalies that could not be properly classified with the AFS system could be effectively classified with the use of the new ESHRE/ESGE system. An electronic literature search through Medline, Embase and Cochrane library was carried out from January 1988 to January 2014. Three participants independently screened, selected articles of potential interest and finally extracted data from all the included studies. Any disagreement was discussed and resolved after consultation with a fourth reviewer and the results were assessed independently and approved by all members of the CONUTA group. PARTICIPANTS/MATERIALS, SETTING, METHODS Among the 143 articles assessed in detail, 120 were finally selected reporting 140 cases that could not properly fit into a specific class of the AFS system. Those 140 cases were clustered in 39 different types of anomalies. MAIN RESULTS AND THE ROLE OF CHANCE The congenital anomaly involved a single organ in 12 (30.8%) out of the 39 types of anomalies, while multiple organs and/or segments of Müllerian ducts (complex anomaly) were involved in 27 (69.2%) types. Uterus was the organ most frequently involved (30/39: 76.9%), followed by cervix (26/39: 66.7%) and vagina (23/39: 59%). In all 39 types, the ESHRE/ESGE classification system provided a comprehensive description of each single or complex anomaly. A precise categorization was reached in 38 out of 39 types studied. Only one case of a bizarre uterine anomaly, with no clear embryological defect, could not be categorized and thus was placed in Class 6 (un-classified) of the ESHRE/ESGE system. LIMITATIONS, REASONS FOR CAUTION The review of the literature was thorough but we cannot rule out the possibility that other defects exist which will also require testing in the new ESHRE/ESGE system. These anomalies, however, must be rare. WIDER IMPLICATIONS OF THE FINDINGS The comprehensiveness of the ESHRE/ESGE classification adds objective scientific validity to its use. This may, therefore, promote its further dissemination and acceptance, which will have a positive outcome in clinical care and research. STUDY FUNDING/COMPETING INTERESTS None.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.