Variability of Manual Segmentation of the Prostate in Axial T2-weighted MRI: A Multi-Reader Study

Purpose To evaluate the interreader variability in prostate and seminal vesicle (SV) segmentation on T2w MRI. Methods Six readers segmented the peripheral zone (PZ), transitional zone (TZ) and SV slice-wise on axial T2w prostate MRI examinations of n = 80 patients. Twenty different similarity scores, including dice score (DS), Hausdorff distance (HD) and volumetric similarity coefficient (VS), were computed with the VISCERAL EvaluateSegmentation software for all structures combined and separately for the whole gland (WG = PZ + TZ), TZ and SV. Differences between base, midgland and apex were evaluated with DS slice-wise. Descriptive statistics for similarity scores were computed. Wilcoxon testing to evaluate differences of DS, HD and VS was performed. Results Overall segmentation variability was good with a mean DS of 0.859 (±SD = 0.0542), HD of 36.6 (±34.9 voxels) and VS of 0.926 (±0.065). The WG showed a DS, HD and VS of 0.738 (±0.144), 36.2 (±35.6 vx) and 0.853 (±0.143), respectively. The TZ showed generally lower variability with a DS of 0.738 (±0.144), HD of 24.8 (±16 vx) and VS of 0.908 (±0.126). The lowest variability was found for the SV with DS of 0.884 (±0.0407), HD of 17 (±10.9 vx) and VS of 0.936 (±0.0509). We found a markedly lower DS of the segmentations in the apex (0.85 ± 0.12) compared to the base (0.87 ± 0.10, p < 0.01) and the midgland (0.89 ± 0.10, p < 0.001). Conclusions We report baseline values for interreader variability of prostate and SV segmentation on T2w MRI. Variability was highest in the apex, lower in the base, and lowest in the midgland

Prediction of pelvic lymph node metastases and PSMA PET positive pelvic lymph nodes with multiparametric MRI and clinical information in primary staging of prostate cancer

PURPOSE To compare the accuracy of multiparametric MRI (mpMRI), $^{68}$Ga-PSMA PET and the Briganti 2019 nomogram in the prediction of metastatic pelvic lymph nodes (PLN) in prostate cancer, to assess the accuracy of mpMRI and the Briganti nomogram in prediction of PET positive PLN and to investigate the added value of quantitative mpMRI parameters to the Briganti nomogram. METHOD This retrospective IRB-approved study included 41 patients with prostate cancer undergoing mpMRI and $^{68}$Ga-PSMA PET/CT or MR prior to prostatectomy and pelvic lymph node dissection. A board-certified radiologist assessed the index lesion on diffusion-weighted (Apparent Diffusion Coefficient, ADC; mean/volume), T2-weighted (capsular contact length, lesion volume/maximal diameters) and contrast-enhanced (iAUC, k$_{ep}$, K$^{trans}$, v$_{e}$) sequences. The probability for metastatic pelvic lymph nodes was calculated using the Briganti 2019 nomogram. PET examinations were evaluated by two board-certified nuclear medicine physicians. RESULTS The Briganti 2019 nomogram performed superiorly (AUC: 0.89) compared to quantitative mpMRI parameters (AUCs: 0.47-0.73) and $^{68}$Ga-PSMA-11 PET (AUC: 0.82) in the prediction of PLN metastases and superiorly (AUC: 0.77) in the prediction of PSMA PET positive PLN compared to MRI parameters (AUCs: 0.49-0.73). The addition of mean ADC and ADC volume from mpMRI improved the Briganti model by a fraction of new information of 0.21. CONCLUSIONS The Briganti 2019 nomogram performed superiorly in the prediction of metastatic and PSMA PET positive PLN, but the addition of parameters from mpMRI can further improve its accuracy. The combined model could be used to stratify patients requiring ePLND or PSMA PET

Quantitative imaging parameters to predict the local staging of prostate cancer in intermediate- to high-risk patients

Objectives: PSMA PET/MRI showed the potential to increase the sensitivity for extraprostatic disease (EPD) assessment over mpMRI; however, the interreader variability for EPD is still high. Therefore, we aimed to assess whether quantitative PSMA and mpMRI imaging parameters could yield a more robust EPD prediction. Methods: We retrospectively evaluated PCa patients who underwent staging mpMRI and [68Ga]PSMA-PET, followed by radical prostatectomy at our institution between 01.02.2016 and 31.07.2019. Fifty-eight cases with PET/MRI and 15 cases with PET/CT were identified. EPD was determined on histopathology and correlated with quantitative PSMA and mpMRI parameters assessed by two readers: ADC (mm2/1000 s), longest capsular contact (LCC, mm), tumor volume (cm3), PSMA-SUVmax and volume-based parameters using a fixed threshold at SUV > 4 to delineate PSMAtotal (g/ml) and PSMAvol (cm3). The t test was used to compare means, Pearson's test for categorical correlation, and ROC curve to determine the best cutoff. Interclass correlation (ICC) was performed for interreader agreement (95% CI). Results: Seventy-three patients were included (64.5 ± 6.0 years; PSA 14.4 ± 17.1 ng/ml), and 31 had EPD (42.5%). From mpMRI, only LCC reached significance (p = 0.005), while both volume-based PET parameters PSMAtotal and PSMAvol were significantly associated with EPD (p = 0.008 and p = 0.004, respectively). On ROC analysis, LCC, PSMAtotal, and PSMAvol reached an AUC of 0.712 (p = 0.002), 0.709 (p = 0.002), and 0.718 (p = 0.002), respectively. ICC was moderate-good for LCC 0.727 (0.565-0.828) and excellent for PSMAtotal and PSMAvol with 0.944 (0.990-0.996) and 0.985 (0.976-0.991), respectively. Conclusions: Quantitative PSMA parameters have a similar potential as mpMRI LCC to predict EPD of PCa, with a significantly higher interreader agreement. Keywords: Extracapsular extension; PSMA PET (MRI) Prostate cancer; Prediction; Seminal vesicle infiltration

Incidence and survival of patients with oligometastatic esophagogastric cancer: A multicenter cohort study

urpose/objective: This multicenter study assessed the incidence and survival of patients with esophagogastric cancer and oligometastatic disease (OMD) in two tertiary referral cancer centers in The Netherlands and Switzerland. Materials/methods: Between 2010 and 2021, patients with metastatic esophagogastric cancer were identified. Patients with de-novo OMD were included (first-time diagnosis of ≤5 distant metastases on 18F-FDG-PET/CT). Control of the primary tumor was considered in patients who underwent primary tumor resection or definitive chemoradiotherapy without locoregional recurrence. Treatment of OMD was categorized into (1) systemic therapy, (2) local treatment (stereotactic body radiotherapy or metastasectomy), (3) local plus systemic therapy, or (4) best supportive care. The primary outcomes were overall survival (OS) and independent prognostic factors for OS. Independent prognostic factors for OS were analyzed using multivariable Cox proportional hazard models. Results: In total, 830 patients with metastatic esophagogastric cancer were identified of whom 200 patients with de-novo OMD were included (24%). The majority of included patients had esophageal cancer (73%) with adenocarcinoma histology (79%) and metachronous OMD (52%). The primary tumor was controlled in 68%. Treatment of OMD was systemic therapy (25%), local treatment (43%), local plus systemic therapy (13%), or best supportive care (18%). Median follow-up was 14 months (interquartile range: 7-27). Median OS was 16 months (95% CI: 13-21). Improved OS was independently associated with local plus systemic therapy compared with systemic therapy alone (hazard ratio [HR] 0.47, 95% confidence interval [CI]: 0.25-0.87). Worse OS was independently associated with squamous cell carcinoma (HR 1.70, 95% CI: 1.07-2.74), bone oligometastases (HR 2.44, 95% CI: 1.28-4.68), brain oligometastases (HR 1.98, 95% CI: 1.05-4.69), and two metastatic locations (HR 2.07, 95% CI: 1.04-4.12). Median OS after local plus systemic therapy was 35 months (95% CI: 22-NA) as compared with 13 months (95% CI: 9-21, p < 0.001) after systemic therapy alone for OMD. Conclusion: Patients with metastatic esophagogastric cancer present in 25% with de-novo OMD. Local treatment of OMD plus systemic therapy was independently associated with long-term OS and independently improved OS when compared with systemic therapy alone. Randomized controlled trials are warranted to confirm these results. Keywords: Esophageal neoplasms; Gastric neoplasms; Lymphatic metastasis; Metastasectomy; Neoplasm metastasis; Radiosurgery

Bundesgerichtliche Urteile zum Arztrecht (2000–2017) – Überblick, ausgewählte Kasuistik und Würdigung

Im Rahmen einer empirischen und kasuistischen Analyse arztrechtlicher Bundesgerichtsurteile zwischen 2000 und 2017 war das Ziel, Tendenzen und Muster der bundesgerichtlichen Rechtsprechung zum Arztrecht zu beschreiben. Unter anderem zeigte das Ergebnis, dass Fachärzte der Inneren Medizin, Psychiatrie, Gynäkologie und Geburtshilfe sowie Chirurgie verhältnismässig häufig involviert sind in bundesgerichtliche Verfahren. Demgegenüber sind Pathologen, Rechtsmediziner oder (diagnostische) Radiologen selten vertreten. Dies kann möglicherweise damit erklärt werden, dass der ersten Gruppe im Verhältnis zu den anderen Facharztrichtungen mehr Ärzte angehören. Des Weiteren wird in der ersten Gruppe entweder stärker gegen den Patientenwillen (z.B. im Rahmen der Zwangsmedikation in der Psychiatrie) vorgegangen oder eine eventuelle Sorgfaltspflichtverletzung ist für den Patienten bzw. seine Angehörigen offensichtlicher bzw. auch ohne medizinische Kenntnisse zumindest erkennbar. = Within the framework of an empirical and casuistic analysis of Federal Court decisions in medical law between 2000 and 2017, the goal was to describe tendencies and patterns of Federal Court decisions in medical law. Among other things, our results show that specialists in internal medicine, psychiatry, gynecology, obstetrics and surgery are more frequently involved in federal court proceedings. By contrast, pathologists, forensic specialists or (diagnostic) radiologists are rarely represented. This may be explained by the fact that the first group consists of more physicians than the other specialties. Furthermore, in the first group, either stronger action may be taken against the will of the patient (e.g. in the context of compulsory medication in psychiatry) or a possible breach of the duty of care is more obvious for the patient or his relatives or at least recognizable even without medical knowledge. = Dans le cadre d’une analyse empirique et casuistique des décisions de la Cour fédérale en droit médical entre 2000 et 2017, l’objectif était de décrire les tendances et les modèles des décisions de la Cour fédérale en droit médical. Entre autres, les résultats montrent que les spécialistes en médecine interne, en psychiatrie, en gynécologie, en obstétrique et en chirurgie sont relativement fréquemment impliqués dans les procédures devant les tribunaux fédéraux. En revanche, les pathologistes, les médecins légistes ou les radiologues (de diagnostic) sont rarement représentés. Cela peut s’expliquer par le fait que le premier groupe comprend plus de médecins que les autres spécialités. En outre, le premier groupe soit prend des mesures plus énergiques contre la volonté du patient (par exemple dans le cadre de la médication obligatoire en psychiatrie), soit une éventuelle violation du devoir de diligence est plus évidente pour le patient ou ses proches ou du moins reconnaissable même sans connaissances médicales

Medicina ex Machina: Machine Learning in der Medizin

Machine Learning (ML) ist ein aktives Forschungsgebiet in den Informationswissenschaften und hat unseren Alltag in den vergangenen Jahren bereits merklich verändert. Mit fortschreitender Entwicklung sind die neuesten Algorithmen imstande, immer komplexere Aufgaben zu übernehmen. Im vorliegenden Mini-Review beschreiben wir einige Grundlagen des ML und zeigen anhand praxisorienterter Beispiele, wie es in den nächsten Jahren Einzug in die klinische Routine halten könnte

Artificial Intelligence und Machine Learning in der Medizin: eine medizinische und rechtliche Würdigung am Beispiel der Radiologie

Gerade in der Radiologie wird der Einsatz von Artificial Intelligence bzw. Machine Learning es ermöglichen, die Arbeitslast der Ärzteschaft zu reduzieren und dadurch z.B. mehr Zeit für komplexere Fälle und für den direkten Patientenkontakt zu ermöglichen. Während sich zumindest die medizinische Forschung bereits intensiv mit dem Machine Learning und dessen Einsatz in der Medizin auseinandersetzt, fehlen weitestgehend äquivalente rechtliche Würdigungen. Es gilt, sich auch aus rechtlicher Perspektive frühzeitig und intensiver mit den damit einhergehenden rechtlichen Herausforderungen auseinanderzusetzen, um den daraus resultierenden Chancen und Gefahren gerecht zu werden