Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development

Sleep disturbances in patients with lung cancer in Turkey

Introduction: Sleep quality is known to be associated with the distressing symptoms of cancer. The purpose of this study was to analyze the impact of cancer symptoms on insomnia and the prevalence of sleep-related problems reported by the patients with lung cancer in Turkey.Materials and Methods: Assesment of Palliative Care in Lung Cancer in Turkey (ASPECT) study, a prospective multicenter study conducted in Turkey with the participation of 26 centers and included all patients with lung cancer, was re-evaluated in terms of sleep problems, insomnia and possible association with the cancer symptoms. Demographic characteristics of patients and information about disease were recorded for each patient by physicians via face-to-face interviews, and using hospital records. Patients who have difficulty initiating or maintaining sleep (DIMS) is associated with daytime sleepiness/fatigue were diagnosed as having insomnia. Daytime sleepiness, fatigue and lung cancer symptoms were recorded and graded using the Edmonton Symptom Assessment Scale.Results: Among 1245 cases, 48.4% reported DIMS, 60.8% reported daytime sleepiness and 82.1% reported fatigue. The prevalence of insomnia was 44.7%. Female gender, patients with stage 3-4 disease, patients with metastases, with comorbidities, and with weight loss > 5 kg had higher rates of insomnia. Also, patients with insomnia had significantly higher rates of pain, nausea, dyspnea, and anxiety. Multivariate logistic regression analysis showed that patients with moderate to severe pain and dyspnea and severe anxiety had 2-3 times higher rates of insomnia.Conclusion: In conclusion, our results showed a clear association between sleep disturbances and cancer symptoms. Because of that, adequate symptom control is essential to maintain sleep quality in patients with lung cancer

Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI.

OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time