Background Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. Methods Five hundred seventy-three consecutive patients (10,448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. Results The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6–94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7–71.3%] to 80.4% [95% CI, 78.0–82.8%], all P < 0.001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. Conclusions BMD enables accurate BM detection. Reading with BMD improves radiologists’ detection sensitivity and reduces their reading times.
BackgroundFew studies have focused on the prognosis of patients with hepatocellular carcinoma (HCC) of Barcelona Clinic Liver Cancer (BCLC) stage 0‒C in terms of early recurrence and 5-years overall survival (OS). We sought to develop nomograms for predicting 5-year OS and early recurrence after curative resection of HCC, based on a clinicopathological‒radiological model. We also investigated whether different treatment methods influenced the OS of patients with early recurrence.MethodsRetrospective data, including clinical pathology, radiology, and follow-up data, were collected for 494 patients with HCC who underwent hepatectomy. Nomograms estimating OS and early recurrence were constructed using multivariate Cox regression analysis, based on the random survival forest (RSF) model. We evaluated the discrimination and calibration abilities of the nomograms using concordance indices (C-index), calibration curves, and Kaplan‒Meier curves. OS curves of different treatments for patients who had recurrence within 2 years after curative surgery were depicted and compared using the Kaplan–Meier method and the log-rank test.ResultsMultivariate Cox regression revealed that BCLC stage, non-smooth margin, maximum tumor diameter, age, aspartate aminotransferase levels, microvascular invasion, and differentiation were prognostic factors for OS and were incorporated into the nomogram with good predictive performance in the training (C-index: 0.787) and testing cohorts (C-index: 0.711). A nomogram for recurrence-free survival was also developed based on four prognostic factors (BCLC stage, non-smooth margin, maximum tumor diameter, and microvascular invasion) with good predictive performance in the training (C-index: 0.717) and testing cohorts (C-index: 0.701). In comparison to the BCLC staging system, the C-index (training cohort: 0.787 vs. 0.678, 0.717 vs. 0.675; external cohort 2: 0.748 vs. 0.624, 0.729 vs. 0.587 respectively, for OS and RFS; external cohort1:0.716 vs. 0.627 for RFS, all p value<0.05), and model calibration curves all showed improved performance. Patients who underwent surgery after tumor recurrence had a higher reOS than those who underwent comprehensive treatments and supportive care.ConclusionsThe nomogram, based on clinical, pathological, and radiological factors, demonstrated good accuracy in estimating OS and recurrence, which can guide follow-up and treatment of individual patients. Reoperation may be the best option for patients with recurrence in good condition.
Background: Numerous factors are related to the prognosis of rectal cancer, including T stage, N stage, metastasis, extramural venous invasion (EMVI), circumferential resection margin (CRM), and tumor differentiation. However, it is still a challenge to precisely evaluate them before therapy; therefore, we investigate whether synthetic magnetic resonance imaging and apparent diffusion coefficient (ADC) values could help predict the prognostic factors of rectal cancer. Methods: Eighty-seven patients (55 men and 32 women; mean age, 59±11 years) with pathologically confirmed rectal cancer were enrolled. Preoperative quantitative metrics, including T1, T2, proton density (PD), and ADC values, were measured with diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and synthetic magnetic resonance imaging acquired by a multi-dynamic multi-echo sequence at 3.0 T, in patients with rectal cancer by two radiologists. We evaluated the diagnostic performance of synthetic magnetic resonance imaging using the independent sample t-test or Mann-Whitney U test and receiver operating characteristic (ROC) curve and multivariate logistic regression analyses and compared the area under the ROC curve of quantitative values using the DeLong test. Results: The T2 and PD values showed a significant reduction among patients with poor differentiation and lymph node metastasis in rectal cancer. The area under the ROC curve values of T2 and PD values for predicting magnetic resonance imaging N stage and differentiation were 0.734, 0.682, and 0.673, 0.686, respectively. Moreover, combining T2 and PD values for magnetic resonance imaging N stage slightly improved the area under the ROC curve value of 0.774 (95% CI, 0.673-0.876). In the present study, the ADC and T1 values were not significant in the differentiation or clinical stage of rectal cancer (RC). Conclusions: Quantitative T2 and PD values obtained by synthetic magnetic resonance imaging might be used for evaluating prognostic factors of rectal cancer noninvasively. Furthermore, combining T2 and PD values further improved the diagnostic performance of magnetic resonance imaging N staging in rectal cancer. The ADC and T1 values were not significant in the differentiation or clinical stage of RC.
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