Added value of positive intraluminal contrast CT over fluoroscopic examination for detecting gastrointestinal leakage after gastrointestinal surgery.

We aimed to evaluate the added value of positive intraluminal contrast computed tomography (CT) over fluoroscopy in detecting anastomotic leakage after gastrointestinal (GI) surgery. A total of 141 GI surgery patients who underwent fluoroscopic examination and CT were included. Two radiologists reviewed the fluoroscopic images with and without CT to determine anastomotic leakage on a 5-point confidence scale and graded the leakage on a 4-point grading system. The hospital stay duration and treatment type were recorded. The radiologists' diagnostic performance in determining leakage was compared using the receiver operating characteristics analysis, and interobserver agreement was analyzed. Fifty-three patients developed GI leakage. When CT was added to the fluoroscopic images, the area under the curve (AUC) values significantly increased for both reviewers. The interobserver agreement for leakage between the two reviewers was excellent and improved with the addition of CT (weighted kappa value, 0.869 versus 0.805). Postoperative intervention was more frequently performed (P < 0.001), and patients with leakage had a significantly longer mean postoperative hospital stay (45 days vs. 27 days) (P = 0.003). Thus, positive intraluminal contrast CT provides added value over fluoroscopic examination for detecting GI leakage in patients undergoing GI tract surgery, increasing AUC values, and improving interobserver agreement.

Prediction of treatment response following ethanol sclerotherapy of renal cysts by computed tomography.

OBJECTIVES: To investigate predictive factors of treatment response following ethanol sclerotherapy of large renal cysts via computed tomography (CT). METHODS: Retrospective study reviewed 71 patients (61.0 ± 13.2 years; M:F = 32:39) who underwent pretreatment CT and were treated with sclerotherapy of a large (> 5 cm) renal cyst (mean volume: 279.8 cc) using 99% ethanol from January 2010 to February 2019. Patients were followed up at least two times, short-term (defined as < 6 months, median 2.1 months) and long-term (defined as > 1 year, median 15.5 months), via ultrasound or CT. Suboptimal response was defined as the volume of residual cyst > 20 mL in each follow-up. Predictive variables of radiologic findings and radiomics features were analyzed using logistic regression analysis. RESULTS: Suboptimal response rates were 33.8% and 18.3% at short-term and long-term follow-ups, respectively. In radiologic findings, patients with suboptimal response in the short-term follow-up showed a more frequent estimated cyst volume ≥ 270 mL (OR 14.8, 95% CI 3.9-55.9, p < 0.001) and sinus protrusion (OR 7.0, 95% CI 1.7-28.5, p = 0.007). Cyst volume ≥ 270 mL was also associated with suboptimal response in the long-term follow-up (OR 4.6, 95% CI 1.3-16.9, p = 0.021). When radiomics features were combined, the area under the curve increased from 0.83 to 0.86 and from 0.68 to 0.82 to predict suboptimal response in short-term and long-term follow-ups, respectively. CONCLUSION: Greater estimated volume, sinus protrusion, and radiomics features of the cysts in pretreatment CT can help predict suboptimal response of renal cyst after sclerotherapy.

Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics.

PURPOSE: This study aims to assess the computed tomography (CT) findings of renal epithelioid angiomyolipoma (EAML) and develop a radiomics-based model for differentiating EAMLs and clear cell renal cell carcinomas (RCCs). METHOD: This two-center retrospective study included 28 histologically confirmed EAMLs and 56 size-matched clear cell RCCs with preoperative three-phase kidney CTs. We conducted subjective image analysis to determine the CT parameters that can distinguish EAMLs from clear cell RCCs. Training and test sets were divided by chronological order of CT scans, and radiomics model was built using ten selected features among radiomics and CT features. The diagnostic performance of the radiomics model was compared with that of the three radiologists using the area under the receiver-operating characteristic curve (AUC). RESULTS: The mean size of the EAMLs was 6.2 ± 5.0 cm. On multivariate analysis, a snowman or ice cream cone tumor shape (OR 16.3; 95% CI 1.7-156.9, P = 0.02) and lower tumor-to-cortex (TOC) enhancement ratio in the corticomedullary phase (OR 33.4; 95% CI 5.7-197, P < 0.001) were significant independent factors for identifying EAMLs. The diagnostic performance of the radiomics model (AUC 0.89) was similar to those of genitourinary radiologists (AUC 0.78 and 0.81, P > 0.05) and superior to that of a third-year resident (AUC 0.63, P = 0.04). CONCLUSIONS: A snowman or ice cream cone shape and lower TOC ratio were more closely associated with EAMLs than with clear cell RCCs. A CT radiomics model was useful for differentiating EAMLs from clear cell RCCs with better diagnostic performance than an inexperienced radiologist.

Preoperative percutaneous needle lung biopsy techniques and ipsilateral pleural recurrence in stage I lung cancer.

OBJECTIVES: A recent meta-analysis of individual patient data revealed that preoperative percutaneous transthoracic needle lung biopsy (PTNB) was associated with an increased risk of ipsilateral pleural recurrence in stage I lung cancer. This study aimed to examine whether particular PTNB techniques reduced the risk of pleural recurrence. METHODS: We retrospectively included 415 consecutive patients with stage I lung cancer who underwent preoperative PTNB and curative resection from 2009 through 2016. Detailed information was collected, including clinical, PTNB technique, radiologic, and pathologic characteristics of lung cancer. Cox regression analyses were performed to identify risk factors for pleural recurrence before and after propensity score matching. RESULTS: The overall follow-up period after PTNB was 62.1 ± 23.0 months, and ipsilateral pleural recurrence occurred in 40 patients. Before propensity score matching, age (p = 0.063), microscopic pleural invasion (p = 0.065), and pathologic tumor size (p = 0.016) tended to be associated with pleural recurrence in univariate analyses and subsequently were matched using a propensity score. After propensity score matching, multivariate analysis revealed that ipsilateral pleural recurrence was associated with a larger target size on computed tomography (hazard ratio [HR] = 1.498; 95% CI, 1.506-2.125; p = 0.023) and microscopic lymphatic invasion (HR = 3.526; 95% CI, 1.491-8.341; p = 0.004). However, no PTNB techniques such as needle gauge, biopsy, or pleural passage numbers were associated with a reduced risk of recurrence. CONCLUSIONS: No particular PTNB techniques were associated with reduced pleural seeding after PTNB in stage I lung cancer. Regardless of the technique, PTNB needs to be cautiously applied when early lung cancer is suspected, followed by curative treatment. KEY POINTS: • Age, microscopic pleural invasion, and pathologic tumor size tended to be associated with pleural recurrence in stage I lung cancer before propensity matching. • After propensity matching, pre-biopsy CT target size and microscopic lymphatic invasion were associated with pleural recurrence. • No particular PTNB techniques were associated with reduced pleural seeding in stage I lung cancer before and after propensity matching.

Addendum to the Acknowledgements: Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

[This corrects the article DOI: 10.2196/11029.].

Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

BACKGROUND: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques. OBJECTIVE: This study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. METHODS: We performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. RESULTS: The mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively. CONCLUSIONS: On the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

Comparison of Wearable Activity Tracker with Actigraphy for Sleep Evaluation and Circadian Rest-Activity Rhythm Measurement in Healthy Young Adults.

OBJECTIVE: The purpose of this study was to evaluate the applicability of data obtained from a wearable activity tracker (Fitbit Charge HR) to medical research. This was performed by comparing the wearable activity tracker (Fitbit Charge HR) with actigraphy (Actiwatch 2) for sleep evaluation and circadian rest-activity rhythm measurement. METHODS: Sixteen healthy young adults (female participants, 62.5%; mean age, 22.8 years) wore the Fitbit Charge HR and the Actiwatch 2 on the same wrist; a sleep log was recorded over a 14-day period. We compared the sleep variables and circadian rest-activity rhythm measures with Wilcoxon signed-rank tests and Spearman's correlations. RESULTS: The periods and acrophases of the circadian rest-activity rhythms and the sleep start times did not differ and correlated significantly between the Fitbit Charge HR and the Actiwatch 2. The Fitbit Charge HR tended to overestimate the sleep durations compared with the Actiwatch 2. However, the sleep durations showed high correlation between the two devices for all days. CONCLUSION: We found that the Fitbit Charge HR showed high accuracy in sleep evaluation and circadian rest-activity rhythm measurement when compared with actigraphy for healthy young adults. The results suggest that the Fitbit Charge HR could be applicable on medical research as an alternative tool to actigraphy for sleep evaluation and measurement of the circadian rest-activity rhythm.