Stefan Candefjord

Technologies for localization and diagnosis of prostate cancer.

The gold standard for detecting prostate cancer (PCa), systematic biopsy, lacks sensitivity as well as grading accuracy. PSA screening leads to over-treatment of many men, and it is unclear whether screening reduces PCa mortality. This review provides an understanding of the difficulties of localizing and diagnosing PCa. It summarizes recent developments of ultrasound (including elastography) and MRI, and discusses some alternative experimental techniques, such as resonance sensor technology and vibrational spectroscopy. A comparison between the different methods is presented. It is concluded that new ultrasound techniques are promising for targeted biopsy procedures, in order to detect more clinically significant cancers while reducing the number of cores. MRI advances are very promising, but MRI remains expensive and MR-guided biopsy is complex. Resonance sensor technology and vibrational spectroscopy have shown promising results in vitro. There is a need for large prospective multicentre trials that unambiguously prove the clinical benefits of these new techniques.

On scene injury severity prediction (OSISP) algorithm for car occupants

Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently on mechanisms of injury in order to reduce undertriage. In this study the value of accident characteristics for field triage is evaluated by developing an on scene injury severity prediction (OSISP) algorithm using only accident characteristics that are feasible to assess at the scene of accident. A multivariate logistic regression model is constructed to assess the probability of a car occupant being severely injured following a crash, based on the Swedish Traffic Accident Data Acquisition (STRADA) database. Accidents involving adult occupants for calendar years 2003-2013 included in both police and hospital records, with no missing data for any of the model variables, were included. The total number of subjects was 29128, who were involved in 22607 accidents. Partition between severe and non-severe injury was done using the Injury Severity Score (ISS) with two thresholds: ISS>8 and ISS>15. The model variables are: belt use, airbag deployment, posted speed limit, type of accident, location of accident, elderly occupant (>55 years old), sex and occupant seat position. The area under the receiver operator characteristic curve (AUC) is 0.78 and 0.83 for ISS>8 and ISS>15, respectively, as estimated by 10-fold cross-validation. Belt use is the strongest predictor followed by type of accident. Posted speed limit, age and accident location contribute substantially to increase model accuracy, whereas sex and airbag deployment contribute to a smaller extent and seat position is of limited value. These findings can be used to refine triage protocols used in Sweden and possibly other countries with similar traffic environments.

Prehospital transportation decisions for patients sustaining major trauma in road traffic crashes in Sweden

ABSTRACT Objective: The objective of this study was to evaluate the proportion and characteristics of patients sustaining major trauma in road traffic crashes (RTCs) who could benefit from direct transportation to a trauma center (TC). Methods: Currently, there is no national classification of TC in Sweden. In this study, 7 university hospitals (UHs) in Sweden were selected to represent a TC level I or level II. These UHs have similar capabilities as the definition for level I and level II TC in the United States. Major trauma was defined as Injury Severity Score (ISS) > 15. A total of 117,730 patients who were transported by road or air ambulance were selected from the Swedish TRaffic Accident Data Acquisition (STRADA) database between 2007 to 2014. An analysis of the patient characteristics sustaining major trauma in comparison with patients sustaining minor trauma (ISS < 15) was conducted. Major trauma patients transported to a TC versus non-TC were further analysed with respect to injured body region and road user type. Results: Approximately 3% (n = 3, 411) of patients sustained major trauma. Thirty-eight percent of major trauma patients were transported to a TC, and 62% were transported to a non-TC. This results in large proportions of patients with Abbreviated Injury Scale (AIS) 3+ injuries being transported to a non-TC.  The number of AIS 3+ head injuries for major trauma patients transported to a TC versus non-TC were similar, whereas a larger number of AIS 3+ thorax injuries were present in the non-TC group. The non-TC major trauma patients had a higher probability of traveling in a car, truck, or bus and to be involved in a crash in a rural location. Conclusions: Our results show that the majority of RTC major trauma patients are transported to a non-TC. This may cause unnecessary morbidity and mortality. These findings can guide the development of improved prehospital treatment guidelines, protocols and decision support systems.

Combining scanning haptic microscopy and fibre optic Raman spectroscopy for tissue characterization.

The tactile resonance method (TRM) and Raman spectroscopy (RS) are promising for tissue characterization in vivo. Our goal is to combine these techniques into one instrument, to use TRM for swift scanning, and RS for increasing the diagnostic power. The aim of this study was to determine the classification accuracy, using support vector machines, for measurements on porcine tissue and also produce preliminary data on human prostate tissue. This was done by developing a new experimental set-up combining micro-scale TRM—scanning haptic microscopy (SHM)—for assessing stiffness on a micro-scale, with fibre optic RS measurements for assessing biochemical content. We compared the accuracy using SHM alone versus SHM combined with RS, for different degrees of tissue homogeneity. The cross-validation classification accuracy for healthy porcine tissue types using SHM alone was 65–81%, and when RS was added it increased to 81–87%. The accuracy for healthy and cancerous human tissue was 67–70% when only SHM was used, and increased to 72–77% for the combined measurements. This shows that the potential for swift and accurate classification of healthy and cancerous prostate tissue is high. This is promising for developing a tool for probing the surgical margins during prostate cancer surgery.

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures. Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ⩾ 16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC). Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks. Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.

A combined tactile and Raman probe for tissue characterization : design considerations

Histopathology is the golden standard for cancer diagnosis and involves the characterization of tissue components. It is labour intensive and time consuming. We have earlier proposed a combined fibre-optic near-infrared Raman spectroscopy (NIR-RS) and tactile resonance method (TRM) probe for detecting positive surgical margins as a complement to interoperative histopathology. The aims of this study were to investigate the effects of attaching an RS probe inside a cylindrical TRM sensor and to investigate how laser-induced heating of the fibre-optic NIR-RS affected the temperature of the RS probe tip and an encasing TRM sensor. In addition, the possibility to perform fibre-optic NIR-RS in a well-lit environment was investigated. A small amount of rubber latex was preferable for attaching the thin RS probe inside the TRM sensor. The temperature rise of the TRM sensor due to a fibre-optic NIR-RS at 270 mW during 20 s was less than 2 °C. Fibre-optic NIR-RS was feasible in a dimmed bright environment using a small light shield and automatic subtraction of a pre-recorded contaminant spectrum. The results are promising for a combined probe for tissue characterization.

Resonance Micro-Raman Investigations of the Rat Medial Preoptic Nucleus: Effects of a Low-Iron Diet on the Neuroglobin Content

The aim of this study was to investigate the medial preoptic nucleus (MPN) of the anterior hypothalamus by resonance Raman spectroscopy (514.5 nm) to determine if it is possible to enhance the Raman scattering of hemoproteins in fresh brain tissue slices. The resonance effect was compared with near-infrared Raman spectra. Two groups of male Sprague Dawley rats were studied, one control group on a normal diet and one group on a low-iron diet to evoke iron deficiency. Each group consisted of four rats, 38–41 days old. The diets lasted for 11, 12, and 15 days. The MPN regions of brain tissue slices were analyzed by monitoring raw and pre-processed mean data, by cluster analysis, and by deriving difference spectra from pre-processed mean spectra. Cluster analysis of the resonance Raman spectra could identify different hemoprotein groups, namely, hemoglobin (Hb) and neuroglobin (Ngb). Spectra from randomly distributed spots revealed high Hb content, whereas Ngb was evenly distributed in the MPN. The different spectra showed a decrease of the Ngb and lipid content for the animals on the low-iron diet. The Ngb decrease was approximately 20%. The data show that resonance Raman spectroscopy is well suited to study hemoproteins in fresh brain tissue.

A wearable microwave detector for diagnosing thoracic injuries-test on a porcine pneumothorax model

Background In the prehospital setting, a point-of-care diagnostic test is needed to diagnose pneumothorax (PTX) and monitor its progression to prevent unnecessary patient morbidity and mortality. Ultrasonography is more sensitive than supine chest x-ray for diagnosing PTX, but the accuracy depends on the experience of the operator. Therefore, a non-operator dependent instrument would be valuable for detection and continuous monitoring of an evolving PTX.

Microwave technology for localization of traumatic intracranial bleedings—A numerical simulation study

Traumatic brain injury (TBI) is a major public health problem worldwide. Intracranial bleedings represents the most serious complication of TBI and need to be surgically evacuated promptly to save lives and mitigate injury. Microwave technology (MWT) is promising as a complement to computed tomography (CT) to be used in road and air ambulances for early detection of intracranial bleedings. In this study, we perform numerical simulations to investigate if a classification algorithm based on singular value decomposition can distinguish between bleedings at different positions adjacent to the skull bone for a similar but simplified problem. The classification accuracy is 94-100% for all classes, a result that encourages us to pursue our efforts with MWT for more realistic scenarios. This indicates that MWT has potential for localizing a detected bleeding, which would increase the diagnostic value of this technique.

Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness

ABSTRACT Objective: Appropriate preprocessing for detecting and removing outlier heartbeats and spectral transformation is essential for deriving heart rate variability (HRV) indices from cardiac monitoring data with high accuracy. The objective of this study is to evaluate agreement between standard preprocessing methods for cardiac monitoring data used to detect outlier heartbeats and perform spectral transformation, in relation to estimating HRV indices for drivers at different stages of sleepiness. Methods: The study analyzed more than 3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. Electrocardiography (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The Pan-Tompkins algorithm was used for peak detection of heartbeats from ECG data. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat interval deviating >30% from the mean of the 4 previous intervals, and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain; these methods were (1) the Fourier transform; (2) an autoregressive model; and (3) the Lomb-Scargle periodogram. The preprocessing methods were compared quantitatively and by assessing agreement between estimations of 13 common HRV indices using Bland-Altman plots and paired Students t-tests. Results: The PC method detected more than 4 times as many outliers (0.28%) than SD (0.065%). Most HRV indices derived using different preprocessing methods exhibited significant systematic (P <.05) and substantial random variations. Conclusions: The standard preprocessing methods for HRV data for outlier heartbeat detection and spectral transformation show low levels of agreement. This finding implies that, prior to designing algorithms for detection of sleepy drivers based on HRV analysis, the impact of different preprocessing methods and combinations thereof on driver sleepiness assessment needs to be studied.