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BACKGROUND AND OBJECTIVE: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. METHODS: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. KEY FINDINGS AND LIMITATIONS: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. CONCLUSIONS AND CLINICAL IMPLICATIONS: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. PATIENT SUMMARY: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.
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PURPOSE: Lymph node involvement in cancer of the uterine cervix is a major independent prognostic factor for overall survival. The aim of our study was to examine the lymphatic drainage regions of the different parts of the uterine cervix. METHODS: An anatomical study of fresh cadavers was conducted by injecting patent blue in the anterior or posterior lip of the uterine cervix and dissecting drainage regions. Furthermore, a retrospective radiological and pathological studies were conducted on patients who were treated for early-stage cancer of the uterine cervix with lymph node involvement. Radiological analysis of pre-therapeutic MRIs and/or pathological analysis of surgical specimens showed the precise location of the tumour. A correlation between the anatomy of the primary lesion site and the lymphatic area involved was established. RESULTS: We administered 15 cadaveric injections: 8 in the anterior lip of the uterine cervix and 7 in the posterior one. For 100% of the anterior lip injections, lymphatic drainage was bilateral ileo-obturator (n = 8/8) combined with bilateral parametrial drainage. For the posterior injections, there was never any ileo-obturator drainage, and 6 of the 7 (75%) posterior injections drained in the posterior uterine serosa. Concerning the clinical study, we included 21 patients. We observed a non-significant tendency towards bilateral lymph node involvement when the tumour of the anterior lip. CONCLUSION: Physiological lymphatic drainage of the uterine cervix presents anatomical specificities, depending on the structure studied, anterior or posterior lip of the cervix. Better knowledge of these specificities should allow personalized surgery for each patient.