scholarly journals Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

10.2196/23026 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23026
Author(s):  
Shengtian Sang ◽  
Ran Sun ◽  
Jean Coquet ◽  
Harris Carmichael ◽  
Tina Seto ◽  
...  
Keyword(s):  
Clinical Judgment ◽  
Predictive Value ◽  
Clinical Care ◽  
Academic Medical Center ◽  
Invasive Mechanical Ventilation ◽  
Viral Pneumonia ◽  
Smoking History ◽  
Predictive Values ◽  
Data Limitations ◽  
Address Data

Background For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective This study aimed to develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods Our framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19–like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results Compared to the COVID-19–like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19–like patients. In the COVID-19–like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19–like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

scholarly journals Learning from past respiratory infections to predict COVID-19 Outcomes: A retrospective study (Preprint)

2020 ◽  
Author(s):  
Shengtian Sang ◽  
Ran Sun ◽  
Jean Coquet ◽  
Haris Carmichael ◽  
Tina Seto ◽  
...  
Keyword(s):  
Clinical Judgment ◽  
Predictive Value ◽  
Invasive Mechanical Ventilation ◽  
Viral Pneumonia ◽  
Smoking History ◽  
White Blood Count ◽  
Predictive Values ◽  
Patient Deterioration ◽  
Data Limitations ◽  
Address Data

BACKGROUND In the clinical care of well-established diseases, randomized trials, literature and research are supplemented by clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, Artificial Intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, lack of clinical data restricts the design and development of such AI tools, particularly in preparation of an impending crisis or pandemic. OBJECTIVE This study aimed to develop and test the feasibility of a ‘patients-like-me’ framework to predict COVID-19 patient deterioration using a retrospective cohort of similar respiratory diseases. METHODS Our framework used COVID-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) from an academic medical center, 2008-2019. Fifteen training cohorts were created using different combinations of the COVID-like cohorts with the ARDS cohort for exploratory purpose. Two machine learning (ML) models were developed, one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS Compared to the COVID-like cohorts (n=16,509), the COVID-19 hospitalized patients (n=125) were significantly younger, with a higher proportion of Hispanic ethnicity, lower proportion of smoking history and fewer comorbidities (P <0.001). COVID-19 patients had a lower IMV rate (15.1 vs 23.2, P=0.016) and shorter time to IMV (2.9 vs 4.1, P <0.001) compared to the COVID-like patients. In the COVID-like training data, the top models achieved excellent performance (AUV > 0.90). Validating in the COVID-19 cohort, the best performing model of predicting IMV was the XGBoost model (AUC: 0.831) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all four COVID-like cohorts without ARDS achieved the best performance (AUC: 0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood count, cardiac troponin, albumin, etc.). Our models suffered from class imbalance, that resulted in high negative predictive values and low positive predictive values. CONCLUSIONS We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

scholarly journals Comparison of the Main Staging Systems for Assessing the Severity of Lung Injury in Patients with COVID-19 and Evaluation of Their Predictive Value

2021 ◽  
Vol 102 (5) ◽  
pp. 296-303
Author(s):  
Y. S. Kudryavtsev ◽  
M. M. Beregov ◽  
A. B. Berdalin ◽  
V. G. Lelyuk
Keyword(s):  
Lung Tissue ◽  
Predictive Value ◽  
Prognostic Value ◽  
Time Course ◽  
Fatal Outcome ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Maximum Magnitude ◽  
Predictive Values ◽  
Staging Systems

Objective: to compare the results of staging the severity of viral pneumonia in patients with COVID-19 based on the results of chest computed tomography (CT) using the empirical visual scale CT 0–4 and chest CT severity score (CT-SS) point scale, as well as to assess their prognostic value.Material and methods. Chest CT scans and anamnestic data in patients hospitalized to a non-specialized center repurposed for the treatment of new coronavirus infection, were analyzed. Chest CT analysis was performed by two radiologists using CT 0–4 and CT-SS scales.Results. The time course of changes in the severity of lung parenchymal lesions, by using both scales, was found to be similar: the maximum magnitude of lung tissue changes was recorded on day 5 of the disease. In cases of death, there was a significantly more extensive lung parenchymal involvement at admission to the center than in recovered patients, which was also true for both CT data assessment systems. Bothscales demonstrated comparable diagnostic and prognostic value: there were no statistically significant differences in sensitivity, specificity, and predictive value of a fatal outcome. Both the CT 0–4 scales and the CT-SS are based on the estimation of the volume of the affected lung tissue, but when the CT 0–4 scale was employed, additional criteria were used in some cases: the presence of hydrothorax and the determination of the maximum score for the most affected lung. Not all patients with a pronounced CT picture of viral pneumonia had a fatal outcome, which may indicate the presence of other factors that increase its risk.Conclusion. Both CT 0–4 and CT-SS scales have similar predictive values. The greater severity of parenchymal damage assessed by these CT scales was associated with the higher mortality rate.

Predictive Value of Decreasing Antithrombin III in Deep-Vein Thrombosis

1980 ◽  
Vol 44 (03) ◽  
pp. 135-137 ◽  
Author(s):  
Thorkild Lund Andreasen
Keyword(s):  
Deep Vein Thrombosis ◽  
Predictive Value ◽  
Coronary Care Unit ◽  
Antithrombin Iii ◽  
Predictive Values ◽  
Vein Thrombosis ◽  
At Iii ◽  
Coronary Care ◽  
Time Of Admission ◽  
Deep Vein

SummaryAntithrombin III (At-III) was measured at the time of admission and two days later in 131 patients laid up in a coronary care unit. The patients were examined for deep-vein thrombosis (DVT) clinically and by means of 125I-fibrinogen scanning. 19 patients developed DVT. In 11 subjects with and 25 without DVT At-III decreased more than 10%. And in 7 with and 17 without DVT At-III decreased more than 15%. One person with DVT had subnormal At-III. By using decrease of At-III or subnormal initial At-III to predict DVT the following predictive value (PV) were found. Decrease ≤ 10%, PV pos.= 0.32 and PV neg. = 0.93. Decrease ≤ 15%, PV pos. = 0.32 and PV neg. = 0.90. The positive predictive values obtained were too low to let decreasing At-III give occasion for prophylactic anticoagulant treatment.

Presepsin - New Marker of Sepsis Romanian Neonatal Intensive Care Unit Experience

2019 ◽  
Vol 70 (8) ◽  
pp. 3008-3013
Author(s):  
Silvia Maria Stoicescu ◽  
Ramona Mohora ◽  
Monica Luminos ◽  
Madalina Maria Merisescu ◽  
Gheorghita Jugulete ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Sepsis ◽  
Predictive Value ◽  
Neonatal Intensive Care ◽  
Complete Blood Count ◽  
Clinical Manifestations ◽  
Receiver Operating Curve ◽  
Predictive Values

Difficulties in establishing the onset of neonatal sepsis has directed the medical research in recent years to the possibility of identifying early biological markers of diagnosis. Overdiagnosing neonatal sepsis leads to a higher rate and duration in the usage of antibiotics in the Neonatal Intensive Care Unit (NICU), which in term leads to a rise in bacterial resistance, antibiotherapy complications, duration of hospitalization and costs.Concomitant analysis of CRP (C Reactive Protein), procalcitonin, complete blood count, presepsin in newborn babies with suspicion of early or late neonatal sepsis. Presepsin sensibility and specificity in diagnosing neonatal sepsis. The study group consists of newborns admitted to Polizu Neonatology Clinic between 15th February- 15th July 2017, with suspected neonatal sepsis. We analyzed: clinical manifestations and biochemical markers values used for diagnosis of sepsis, namely the value of CRP, presepsin and procalcitonin on the onset day of the disease and later, according to evolution. CRP values may be influenced by clinical pathology. Procalcitonin values were mainly influenced by the presence of jaundice. Presepsin is the biochemical marker with the fastest predictive values of positive infection. Presepsin can be a useful tool for early diagnosis of neonatal sepsis and can guide the antibiotic treatment. Presepsin value is significantly higher in neonatal sepsis compared to healthy newborns (939 vs 368 ng/mL, p [ 0.0001); area under receiver operating curve (AUC) for presepsine was 0.931 (95% confidence interval 0.86-1.0). PSP has a greater sensibility and specificity compared to classical sepsis markers, CRP and PCT respectively (AUC 0.931 vs 0.857 vs 0.819, p [ 0.001). The cut off value for presepsin was established at 538 ng/mLwith a sensibility of 79.5% and a specificity of 87.2 %. The positive predictive value (PPV) is 83.8 % and negative predictive value (NPV) is 83.3%.

scholarly journals Diagnosis of tuberculosis pleurisy with three endoscopic features via pleuroscopy

2021 ◽  
Vol 15 ◽  
pp. 175346662198953
Author(s):  
Chung-Shu Lee ◽  
Shih-Hong Li ◽  
Chih-Hao Chang ◽  
Fu-Tsai Chung ◽  
Li-Chung Chiu ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Predictive Value ◽  
Image Interpretation ◽  
Discrete Distribution ◽  
Unknown Origin ◽  
Tertiary Hospital ◽  
Public Health Campaign ◽  
Health Campaign ◽  
Predictive Values ◽  
Characteristic Features

Background: Tuberculosis (TB) is a constant threat even with a worldwide active public health campaign. Diagnosis of TB pleurisy is challenging in the case of pleural effusion of unknown origin after aspiration analysis. The study was designed to demonstrate a simple image interpretation technique to differentiate TB pleurisy from non-TB pleurisy using semi-rigid pleuroscopy. Methods: The study retrospectively enrolled 117 patients who underwent semi-rigid pleuroscopy from April 2016 to August 2018 in a tertiary hospital. We analyzed the possibility of TB pleurisy using three simple pleuroscopic images via semi-rigid pleuroscopy. Results: Among 117 patients, 28 patients (23.9%) were diagnosed with TB pleurisy. Sago-like nodules/micronodules, adhesion, and discrete distribution were noted in 20 (71.4%), 20 (71.4%), and 19 (67.9%) patients with TB pleurisy, respectively. Sago-like nodules/micronodules, adhesion, and discrete distribution were noted in six (6.7%), 37 (41.6%), and no (0.0%) patients with non-TB pleurisy, respectively. The positive and negative predictive values of any two out of three pleuroscopic patterns for TB pleurisy were 100.0% and 93.7%, respectively. Conclusions: A high positive predictive value for TB pleurisy was demonstrated by the presence of any two out of the three characteristic features. Absence of all three features had an excellent negative predictive value for TB pleurisy. Our diagnostic criteria reconfirm that pleuroscopic images can be used as predictors for TB pleurisy in patients with undiagnosed pleural effusion. The reviews of this paper are available via the supplementary material section.

scholarly journals Risk factors associated with physician trainee concern over missed educational opportunities during the COVID-19 pandemic

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sunny S. Lou ◽  
Charles W. Goss ◽  
Bradley A. Evanoff ◽  
Jennifer G. Duncan ◽  
Thomas Kannampallil
Keyword(s):  
Risk Factors ◽  
Clinical Education ◽  
Medical Center ◽  
Clinical Care ◽  
Academic Medical Center ◽  
Clinical Work ◽  
Educational Opportunities ◽  
Missed Opportunities ◽  
Physician Trainee ◽  
At Home

Abstract Background The COVID-19 pandemic resulted in a transformation of clinical care practices to protect both patients and providers. These changes led to a decrease in patient volume, impacting physician trainee education due to lost clinical and didactic opportunities. We measured the prevalence of trainee concern over missed educational opportunities and investigated the risk factors leading to such concerns. Methods All residents and fellows at a large academic medical center were invited to participate in a web-based survey in May of 2020. Participants responded to questions regarding demographic characteristics, specialty, primary assigned responsibility during the previous 2 weeks (clinical, education, or research), perceived concern over missed educational opportunities, and burnout. Multivariable logistic regression was used to assess the relationship between missed educational opportunities and the measured variables. Results 22% (301 of 1375) of the trainees completed the survey. 47% of the participants were concerned about missed educational opportunities. Trainees assigned to education at home had 2.85 [95%CI 1.33–6.45] greater odds of being concerned over missed educational opportunities as compared with trainees performing clinical work. Trainees performing research were not similarly affected [aOR = 0.96, 95%CI (0.47–1.93)]. Trainees in pathology or radiology had 2.51 [95%CI 1.16–5.68] greater odds of concern for missed educational opportunities as compared with medicine. Trainees with greater concern over missed opportunities were more likely to be experiencing burnout (p = 0.038). Conclusions Trainees in radiology or pathology and those assigned to education at home were more likely to be concerned about their missed educational opportunities. Residency programs should consider providing trainees with research or at home clinical opportunities as an alternative to self-study should future need for reduced clinical hours arise.

Predictive value of time-variant color-coded multiphase CT angiography (mCTA) regarding clinical outcome of acute ischemic stroke: in comparison with conventional mCTA and CT perfusion

2020 ◽  
pp. 028418512098177
Author(s):  
Yu Lin ◽  
Nannan Kang ◽  
Jianghe Kang ◽  
Shaomao Lv ◽  
Jinan Wang
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Predictive Value ◽  
Ct Perfusion ◽  
Roc Curves ◽  
Multivariate Logistic Regression Model ◽  
Infarct Core ◽  
Predictive Values ◽  
Significant Difference ◽  
Core Volume

Background Color-coded multiphase computed tomography angiography (mCTA) can provide time-variant blood flow information of collateral circulation for acute ischemic stroke (AIS). Purpose To compare the predictive values of color-coded mCTA, conventional mCTA, and CT perfusion (CTP) for the clinical outcomes of patients with AIS. Material and Methods Consecutive patients with anterior circulation AIS were retrospectively reviewed at our center. Baseline collateral scores of color-coded mCTA and conventional mCTA were assessed by a 6-point scale. The reliabilities between junior and senior observers were assessed by weighted Kappa coefficients. Receiver operating characteristic (ROC) curves and multivariate logistic regression model were applied to evaluate the predictive capabilities of color-coded mCTA and conventional mCTA scores, and CTP parameters (hypoperfusion and infarct core volume) for a favorable outcome of AIS. Results A total of 138 patients (including 70 cases of good outcomes) were included in our study. Patients with favorable prognoses were correlated with better collateral circulations on both color-coded and conventional mCTA, and smaller hypoperfusion and infarct core volume (all P < 0.05) on CTP. ROC curves revealed no significant difference between the predictive capability of color-coded and conventional mCTA ( P = 0.427). The predictive value of CTP parameters tended to be inferior to that of color-coded mCTA score (all P < 0.001). Both junior and senior observers had consistently excellent performances (κ = 0.89) when analyzing color-coded mCTA maps. Conclusion Color-coded mCTA provides prognostic information of patients with AIS equivalent to or better than that of conventional mCTA and CTP. Junior radiologists can reach high diagnostic accuracy when interpreting color-coded mCTA images.

scholarly journals Screening for aneurysms of the abdominal aorta using a simple screening device

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
J. W. Brakel ◽  
T. A. Berendsen ◽  
P. M. C. Callenbach ◽  
J. van der Burgh ◽  
R. J. Hissink ◽  
...  
Keyword(s):  
Negative Predictive Value ◽  
Sensitivity And Specificity ◽  
Abdominal Aorta ◽  
Predictive Value ◽  
Cost Effective ◽  
Abdominal Ultrasound ◽  
Predictive Values ◽  
Bladder Scan ◽  
Health Council ◽  
Screening Device

Abstract Introduction Several countries advocate screening for aneurysms of the abdominal aorta (AAA) in selected patients. In the Netherlands, routine screening is currently under review by the National Health Council. In any screening programme, cost-efficiency and accuracy are key. In this study, we evaluate the Aorta Scan (Verathon, Amsterdam, Netherlands), a cost-effective and easy-to-use screening device based on bladder scan technology, which enables untrained personnel to screen for AAA. Methods We subjected 117 patients to an Aorta Scan and compared the results to the gold standard (abdominal ultrasound). We used statistical analysis to determine sensitivity and specificity of the Aorta Scan, as well as the positive and negative predictive values, accuracy, and inter-test agreement (Kappa). Results Sensitivity and specificity were 0.86 and 0.98, respectively. Positive predictive value was 0.98 and negative predictive value was 0.88. Accuracy was determined at 0.92 and the Kappa value was 0.85. When waist–hip circumferences (WHC) of > 115 cm were excluded, sensitivity raised to 0.96, specificity stayed 0.98, positive and negative predictive value were 0.98 and 0.96, respectively, accuracy to 0.97, and Kappa to 0.94. Conclusion Herein, we show that the Aorta Scan is a cost-effective and very accurate screening tool, especially in patients with WHC below 115 cm, which makes it a suitable candidate for implementation into clinical practice, specifically in the setting of screening selected populations for the presence of AAA.

scholarly journals 781. C.difficile PCR+/ Toxin EIA- treat or not treat? A clinician survey

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S435-S436
Author(s):  
Sarath G Nath ◽  
Francesca Lee ◽  
Anjali Bararia ◽  
Ank E Nijhawan
Keyword(s):  
Clinical Judgment ◽  
Clinical Care ◽  
Cost Effective ◽  
False Positive Diagnosis ◽  
Missed Diagnosis ◽  
Stool Samples ◽  
Polymerase Chain ◽  
Low Sensitivity ◽  
Survey Responses ◽  
And Training

Abstract Background C.difficile Toxin Polymerase Chain Reaction (C.diff PCR) and C.difficile Toxin Enzyme Immunoassays (toxin EIA) are commonly used tests to diagnose Clostridoides difficile infection (CDI). C.diff PCR cannot differentiate between colonization and infection, leading to a higher false-positive diagnosis of CDI. Toxin EIA has low sensitivity leading to a missed diagnosis of CDI. In patients with C.diff PCR positive(+) and Toxin EIA negative(-), clinical judgment is often needed regarding the decision to treat or not to treat. C.diff cytotoxic assay (CCA), is a more sensitive method to detect the toxin but is time-consuming and not readily available. Methods Between 6/2019 and 12/2019, 83 patients who were admitted to the hospital, met our inclusion criteria (C.diff PCR+/EIA-). Clinicians who cared for these patients were contacted and surveyed with a predesigned questionnaire evaluating the rationale of treatment. Also, a simultaneous medical records review was done to ensure consistency. Along with this C.diff PCR+/EIA- stool samples were sent to ARUP laboratories for CCA. The CCA results were not available for clinicians and did not impact clinical care. Average cost for a CCA assay was $29 Results Demographics of the clinicians were variable (Table 1). Several parameters were considered when making decisions regarding treatment and GI/ID were frequently involved (figure 1). Among the 83 patients, 41(49%) were CCA (+) and 42(51%) were CCA (-). 48 of 83 (58%) patients received treatment for CDI. 25 of 48 (52%) patients who were treated were CCA positive while 23 of 48 (48%) patients were CCA negative. Among the untreated patients, 16/35 (46%) were CCA+ while 19/35(54%) were CCA-. There was no statistically significant correlation between clinical judgment and CCA assay results (p: 0.56 on the Chi test). Demographics of the clinicians Clinician survey responses CDI Treatment and by CCA positivity Conclusion Clinicians regardless of their background and training face challenges with the treatment of C.diff PCR+/EIA- patients. Patient outcomes based on the incorporation of CCA assay into an algorithm for C.diff PCR+/EIA- patients, need to be evaluated. But it has a potential role in stopping unnecessary CDI treatment as well as avoidance of missed treatment opportunities while possibly also being cost-effective. Disclosures Ank E. Nijhawan, MD, MPH, Gilead (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)

scholarly journals Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 563
Author(s):  
Chen Shenhar ◽  
Hadassa Degani ◽  
Yaara Ber ◽  
Jack Baniel ◽  
Shlomit Tamir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Predictive Value ◽  
Breast Imaging ◽  
Normal Tissue ◽  
Diffusion Tensor ◽  
Peripheral Zone ◽  
Predictive Values ◽  
Directional Diffusion ◽  
Central Gland

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

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