Background AimTo gain insight into patient and doctor delay in testicular cancer (TC) and factors associated with delay.Materials and MethodsSixty of the 66 eligible men; median age 26 (range 17–45) years, diagnosed with TC at the University Medical Center Groningen completed a questionnaire on patients’ delay: interval from symptom onset to first consultation with a general practitioner (GP) and doctors’ delay: interval between GP and specialist visit.ResultsMedian patient reported delay was 30 (range 1–365) days. Patient delay and TC tumor stage were associated (p = .01). Lower educated men and men embarrassed about their scrotal change reported longer patient delay (r = -.25, r = .79 respectively). Age, marital status, TC awareness, warning signals, nor perceived limitations were associated with patient delay. Median patient reported time from GP to specialist (doctors’ delay) was 7 (range 0–240) days. Referral time and disease stage were associated (p = .04). Six patients never reported a scrotal change. Of the 54 patients reporting a testicular change, 29 (54%) patients were initially ‘misdiagnosed’, leading to a median doctors’ delay of 14 (1–240) days, which was longer (p< .001) than in the 25 (46%) patients whose GP suspected TC (median doctors’ delay 1(0–7 days).ConclusionsHigh variation in patients’ and doctors’ delay was found. Most important risk variables for longer patient delay were embarrassment and lower education. Most important risk variable in GP’s was ‘misdiagnosis’. TC awareness programs for men and physicians are required to decrease delay in the diagnosis of TC and improve disease free survival.

αβ T-cell-depleted haploidentical transplantation may be a good alternative for high-risk patients if there are no human leukocyte antigen matched donors.

BackgroundThere is a tendency toward nonoperative management of appendicitis resulting in an increasing need for preoperative diagnosis and classification. For medical purposes, simple conceptual decision-making models that can learn are widely used. Decision trees are reliable and effective techniques which provide high classification accuracy. We tested if we could detect appendicitis and differentiate uncomplicated from complicated cases using machine learning algorithms. MethodsWe analyzed all cases admitted between 2010 and 2016 that fell into the following categories: healthy controls (Group 1); sham controls (Group 2); sham disease (Group 3), and acute abdomen (Group 4). The latter group was further divided into four groups: false laparotomy; uncomplicated appendicitis; complicated appendicitis without abscess, and complicated appendicitis with abscess. Patients with comorbidities and whose complete blood count and/or pathology results were lacking were excluded. Data were collected for demographics, preoperative blood analysis, and postoperative diagnosis. Various machine learning algorithms were applied to detect appendicitis patients. ResultsThere were 7244 patients with a mean age of 6.84±5.31 years, of whom 82.3% (5960/7244) were male. Most algorithms tested, especially linear methods, provided similar performance measures. We preferred the decision tree model due to its easier interpretability. With this algorithm, we detected appendicitis patients with 93.97 % area under the curve (AUC), 94.69% accuracy, 93.55% sensitivity, and 96.55% specificity, and uncomplicated appendicitis with 79.47% AUC, 70.83% accuracy, 66.81% sensitivity, and 81.88% specificity. ConclusionsMachine learning is a novel approach to prevent unnecessary operations and decrease the burden of appendicitis both for patients and health systems.

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