The prevalence of radial scar (RS) is 0.04% in asymptomatic women participating in population screening for breast cancer. It is important to differentiate RS from concomitant malignancies, which occur in 20-30% of patients, or from small stellate carcinomas which give similar radiomorphology. The aim of our study was to evaluate the effectivity of current breast diagnostic methods in distinguishing between real RS, concomitant malignancy and carcinomas imitating RS. Diagnosis of RS was set up in 61 cases by mammography. Forty-four patients underwent surgical excision: histology showed benign or malignant lesions in 28 and 16 cases, respectively. A series of negative results at follow-up proved the benign nature of the lesion in further 11 cases. Six patients were not available for follow-up. Results of mammography, physical examination, ultrasonography and cytology were evaluated and were compared in 39 benign and 16 malignant cases. Results of examinations were reported on the BI-RADS scale ranging from 1 to 5. The mean categorical scores of all diagnostic processes were around the level of borderline lesions: mammography: 3.49, ultrasonography: 3.06, cytology: 2.47 and physical examination: 1.67. The average age of the patients in the benign and malignant groups were the same: 58 years. The two groups did not differ significantly over either distribution of coded mammographical results (p = 0.2092), or the distribution of mammographical parenchyma density patterns (p = 0.4875). However, the malignant and benign groups differed significantly from each other over the distribution of coded ultrasonographic (p = 0.0176) and cytological (p < 0.0001) results. In conclusion, in the preoperative diagnosis of asymptomatic "black-stars", mammography detects the non-palpable lesions, and ultrasonography together with cytology proved better in the analysis, provided FNAB is US guided. Due to the complex diagnostic approach the nature of the "black stars" is known in the majority of cases prior to the surgical biopsy.
The aim of our study was to compare the preoperative sum score diagnostics of invasive ductal and lobular cancers using three or four diagnostic methods. The novelty of this study is the examination of this phenomenon based on sum score, no such papers can be found in the literature. Ductal cancers have higher score values indicating easier diagnostics, but the difference in distribution of the scores was significant (p = 0.0086) only in case of the triple-test. The score values give appropriate opportunity to create their order of diagnostic power which was the same by both histologic types and in their subgroups with low sum-score: the strongest was cytology, followed by mammography, ultrasound and physical examination. No significant difference was found between the two histologic group in their mammographic appearances-stellate, circumscribed, assymmetric distortion or microcalcification-(p = 0.0694). In low score subgroup besides the occult forms, structural distortion and indeterminate microcalcifications overweighed the stellate and circumscribed lesions typical for the whole groups. In symptomless cases of both histologic groups only one strongly malignant diagnostic test result warrants the right diagnosis. Summarizing the score distribution of the results in case of four diagnostic tools the higher scores-indicating malignancy-were more frequent in the ductal group compared to the lobular ones. Extra attention has to be paid to rare radiomorphologic appearances and to the most deterministic examination, namely cytology.
Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.
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