Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response

2018 ◽  
pp. 803-811 ◽  
Author(s):  
Nathaniel Braman ◽  
Prateek Prasanna ◽  
Mehdi Alilou ◽  
Niha Beig ◽  
Anant Madabhushi
Keyword(s):  
Treatment Response ◽  
Hough Transform ◽  
Vascular Network ◽  
Diagnosis And Treatment ◽  
Network Organization

Treatment response as an aid in diagnosis and treatment planning

1970 ◽  
Vol 57 (5) ◽  
pp. 490-496 ◽  
Author(s):  
James L. Ackerman ◽  
William R. Proffit
Keyword(s):  
Treatment Response ◽  
Treatment Planning ◽  
Diagnosis And Treatment

scholarly journals High Mycobacterium tuberculosis Bacillary Loads Detected by Tuberculosis Molecular Bacterial Load Assay in Patient Stool: a Potential Alternative for Nonsputum Diagnosis and Treatment Response Monitoring of Tuberculosis

2022 ◽  
Author(s):  
Emmanuel Musisi ◽  
Abdul Sessolo ◽  
Sylvia Kaswabuli ◽  
Josephine Zawedde ◽  
Patrick Byanyima ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Treatment Response ◽  
Bacterial Load ◽  
Diagnosis And Treatment ◽  
Response Monitoring ◽  
Sample Type ◽  
Content Type ◽  
Potential Alternative

This paper highlights the value of stool as a sample type for diagnosis of tuberculosis. While other studies have used DNA-based assays like the Xpert MTB/RIF and culture to detect Mycobacterium tuberculosis in stool, this is the first study that has applied TB-MBLA, an RNA-based assay, to quantify TB bacteria in stool.

scholarly journals The Impact of the COVID-19 Pandemic on the Diagnosis and Management of Patients with Classical Hodgkin Lymphoma: A Single-Center Experience

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1394-1394
Author(s):  
Selin Kucukyurt ◽  
Kübra Şahin ◽  
Umut Yılmaz ◽  
Abdülkadir Erçalışkan ◽  
Tuba Özkan Tekin ◽  
...  
Keyword(s):  
Treatment Response ◽  
Symptom Onset ◽  
Treatment Initiation ◽  
Early Stage ◽  
The Other ◽  
Diagnosis And Treatment ◽  
Time Interval ◽  
Classical Hodgkin Lymphoma ◽  
Single Center ◽  
Stage Disease

Abstract Background: The coronavirus disease 2019 (COVID-19) pandemic has brought life to a standstill all over the world. Especially in Istanbul, the most populated city in Turkey, many hospitals served as pandemic hospitals and suspended the examination of patients who were not infected with COVID-19. On the other hand, in this period, lockdown was applied frequently to control the pandemic and to reduce the transmission. It is known that the delay in the diagnosis and treatment of cancer can sometimes be fatal. Initial treatment choice in classical Hodgkin lymphoma (cHL) patients is determined by various factors including the stage of the disease. Therefore, detection of the disease at an early stage has prognostic importance. Our aim was to compare the time from symptom onset to diagnosis and period between diagnosis and treatment initiation, as well as the distribution of stages and treatment responses in cHL patients diagnosed and treated during the COVID-19 pandemic and in the pre-pandemic period. Methods: Patients who were diagnosed within the 2 years before the pandemic (between March 2018 and March 2020) and in a 12-month period during the pandemic (between March 2020 and March 2021) were compared in terms of demographic data, disease related factors, time interval between symptom to diagnosis, and interim treatment response. Clinical data were obtained from manual and electronic medical records retrospectively. PET scans were performed at baseline, after two cycles of chemotherapy, and end of treatment (EOT). The Deauville five-point scale (5-PS) was used in the initial staging and assessment of treatment response. The 5-PS; a score of 1, 2, or 3 was considered negative. The 5-PS score 4 or 5 was positive. Results: This single-center study included 90 newly diagnosed cHL patients, with a median age of 33.5 years (range, 17 - 70 years) and a male predominance (53.3%). The most common presenting symptom was a lump in the neck (41.1%), and also, 61.1% (n=55) of the cases had at least one B symptom. Patient characteristics were summarized in Table 1. Age and sex distributions were similar in both groups. Also, the number of patients >60 years was comparable (p=0.868). The most common histopathological subtype in both groups was nodular sclerosis (47.7% vs. 48%). In the pre-pandemic period and during the pandemic, the percentages of patients with early unfavorable disease were 66.7% and 77.8%, respectively (p=0.526). Although the percentage of patients with advanced-stage disease was higher during the pandemic than that observed in the pre-pandemic period (64% vs. 53.8%), this difference did not reach statistical significance (p=0.384). The median interval between symptom onset to diagnosis was significantly longer during the pandemic than was observed within the pre-pandemic era (16 weeks vs. 8 weeks, p=0.042). The median durations between diagnosis and treatment initiation were similar in both groups (13 days vs. 15 days, p=0.253). The majority of patients in both groups received ABVD as first-line therapy, and IFRT was performed in some patients with early-stage cHL (Figure 1). Among all patients (n=90), 85% of the cases had negative interim PET scan results, and this percentage was similar for both patient groups (84.6% vs. 88%, p=0.999). In the pre-pandemic period, 80% of the patients had complete response at EOT, on the other hand, no comparison was performed between two groups regarding response level at EOT, since nearly half of patients (48%) treated during the pandemic were not evaluated for the EOT response at the time of the analysis. Discussion & Conclusion: In our cohort, the time interval between symptom onset to diagnosis was significantly prolonged during the pandemic. Most probably the patients were afraid of admitting to the hospital due to the fear of contagion, or patients might have experienced difficulties in applying to a health facility, and lastly maybe they had tolerable symptoms, which might all have roles in the diagnostic delay during the pandemic. Higher percentage of patients with advanced-stage disease during the pandemic might also be associated with this delay, however, fortunately, this difference did not translate into a significant difference regarding interim treatment response in both groups. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adam Penn-Nicholson ◽  
◽  
Stanley Kimbung Mbandi ◽  
Ethan Thompson ◽  
Simon C. Mendelsohn ◽  
...  
Keyword(s):  
Treatment Response ◽  
Disease Risk ◽  
Diagnosis And Treatment ◽  
Transcriptomic Signature

Prediction of diagnosis and treatment response in adolescents with depression using smartphone application and machine learning approaches: a pilot study (Preprint)

2020 ◽  
Author(s):  
Jae Sung Kim ◽  
Bohyun Wang ◽  
Meelim Kim ◽  
Jung Lee ◽  
Hyungjun Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mobile Phone ◽  
Treatment Response ◽  
Antidepressant Treatment ◽  
Smartphone Application ◽  
Text Messages ◽  
Diagnosis And Treatment ◽  
Learning Approaches ◽  
Smart Healthcare

BACKGROUND Lack of quantifiable biomarkers is a major obstacle in making diagnosis and predicting treatment response in depression. In adolescents, increasing suicidality during antidepressant treatment further complicate the problems. Emerging healthcare systems based on digital technology are beginning to show promising results in dealing with mental health issues. OBJECTIVE Using Smart Healthcare System for Teens At Risk for Depression and Suicide (STAR-DS) smartphone application and machine learning, we sought to evaluate digital phenotypes which represent the diagnosis and treatment response of depression in adolescents. METHODS Our study included 24 adolescents (15.4±1.4 years, 17 girls) with major depressive disorder (MDD) diagnosed with K-SADS-PL and 10 healthy controls (13.8±0.6 years, 5 girls). Their depression status was evaluated using the Children’s Depression Rating Scale–Revised (CDRS-R) and CGI-S every week during the study period. After collecting the baseline data for 1 week, MDD adolescents were treated with escitalopram in an 8 week, open-label trial. Both MDD and control groups were monitored for another 4 weeks after the baseline week. We applied deep learning approach for the analysis of data. Deep Neural Network (DNN) was employed for classification and NEural network with Weighted Fuzzy Membership functions (NEWFM) for feature selection. We extracted features from directly collected data via the mobile phone (the number and total time of calls and text messages sent or received, mobile phone usage time, movement distance, amount of activity measured by gyroscope) on a daily basis. The distance from the mean value and standard deviation of each features per week were also extracted. RESULTS We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of 24 depressed adolescents, 10 responded to antidepressant treatment. Including data on medications taken by the MDD group, we predicted the treatment response of depressed adolescents with training accuracy of 94.2% and 3-fold validation accuracy of 76%. CONCLUSIONS The STAR-DS smartphone application demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict treatment response of MDD in adolescents, examining smartphone based objective data with machine learning approaches.

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