Treatment Approaches for Melanomas That Relapse After Adjuvant or Neoadjuvant Therapy.

PURPOSE OF REVIEW: Effective adjuvant treatment with immunotherapy and targeted therapy has significantly improved outcomes for patients with resectable locally advanced or metastatic melanoma, but a substantial proportion unfortunately relapse. Here, we review available data and explore evolving research which might impact decision-making in this setting. RECENT FINDINGS: Small retrospective studies have explored pattern of disease relapse and observed outcomes of subsequent treatment. There are ongoing trials in the neoadjuvant setting which may provide valuable information regarding disease response and potentially change the way we approach disease relapse. Currently there is limited evidence to guide clinicians in managing melanomas that relapse after adjuvant therapy. Standardised data collection and future prospective studies are needed.

Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.

Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.

Mind perception and stereotype attribution of corporations and charities.

People generally attribute less mind to groups than to individuals. Previous research has also shown differences of mind perception between different types of groups, such that not-for-profit organizations were viewed as having more minds than for-profit organizations. In this paper, we ascertained this mind perception differences and further examined its underlying mechanisms and concomitant consequences. Across three studies, we replicated that people attributed more mind to not-for-profit organizations than to for-profit organizations. More critically, the current research linked mind perception to stereotype content model and added that this effect was mainly explained by perceived warmth of the groups rather than perceived competence. We found that although not-for-profit organizations were perceived as warmer but less competent than for-profit ones, the former was perceived both as having more experiential and agentic mental capacities than the latter. In addition, for-profit organizations received less compassionate responding than not-for-profit ones when they suffer, which was attributable to mind perception. This paper highlights the distinction between experience and warmth, and between agency and competence, thus extending our theoretical understanding of the fundamental dimensions of mind perception and those of the stereotype content of groups.

Patient-Derived Explants as a Precision Medicine Patient-Proximal Testing Platform Informing Cancer Management.

Precision medicine approaches that inform clinical management of individuals with cancer are progressively advancing. Patient-derived explants (PDEs) provide a patient-proximal ex vivo platform that can be used to assess sensitivity to standard of care (SOC) therapies and novel agents. PDEs have several advantages as a patient-proximal model compared to current preclinical models, as they maintain the phenotype and microenvironment of the individual tumor. However, the longevity of PDEs is not compatible with the timeframe required to incorporate candidate therapeutic options identified by whole exome sequencing (WES) of the patient's tumor. This review investigates how PDE longevity varies across tumor streams and how this is influenced by tissue preparation. Improving longevity of PDEs will enable individualized therapeutics testing, and thus contribute to improving outcomes for people with cancer.

Enhancement of needle visualization and localization in ultrasound.

PURPOSE: This scoping review covers needle visualization and localization techniques in ultrasound, where localization-based approaches mostly aim to compute the needle shaft (and tip) location while potentially enhancing its visibility too. METHODS: A literature review is conducted on the state-of-the-art techniques, which could be divided into five categories: (1) signal and image processing-based techniques to augment the needle, (2) modifications to the needle and insertion to help with needle-transducer alignment and visibility, (3) changes to ultrasound image formation, (4) motion-based analysis and (5) machine learning. RESULTS: Advantages, limitations and challenges of representative examples in each of the categories are discussed. Evaluation techniques performed in ex vivo, phantom and in vivo studies are discussed and summarized. CONCLUSION: Greatest limitation of the majority of the literature is that they rely on original visibility of the needle in the static image. Need for additional/improved apparatus is the greatest limitation toward clinical utility in practice. SIGNIFICANCE: Ultrasound-guided needle placement is performed in many clinical applications, including biopsies, treatment injections and anesthesia. Despite the wide range and long history of this technique, an ongoing challenge is needle visibility in ultrasound. A robust technique to enhance ultrasonic needle visibility, especially for steeply inserted hand-held needles, and while maintaining clinical utility requirements is needed.

The effect of subgroup homogeneity of efficacy on contribution in public good dilemmas.

This paper examines how to maximize contribution in public good dilemmas by arranging people into homogeneous or heterogeneous subgroups. Past studies on the effect of homogeneity of efficacy have exclusively manipulated group composition in their experimental designs, which might have imposed a limit on ecological validity because group membership may not be easily changed in reality. In this study, we maintained the same group composition but varied the subgroup composition. We developed a public good dilemmas paradigm in which participants were assigned to one of the four conditions (high- vs. low-efficacy; homogeneous vs. heterogeneous subgroup) to produce their endowments and then to decide how much to contribute. We found that individuals in homogeneous and heterogeneous subgroups produced a similar amount and proportion of contribution, which was due to the two mediating effects that counteracted each other, namely (a) perceived efficacy relative to subgroup and (b) expectation of contribution of other subgroup members. This paper demonstrates both the pros and cons of arranging people into homogeneous and heterogeneous subgroups of efficacy.

Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks.

PURPOSE: During needle interventions, successful automated detection of the needle immediately after insertion is necessary to allow the physician identify and correct any misalignment of the needle and the target at early stages, which reduces needle passes and improves health outcomes. METHODS: We present a novel approach to localize partially inserted needles in 3D ultrasound volume with high precision using convolutional neural networks. We propose two methods based on patch classification and semantic segmentation of the needle from orthogonal 2D cross-sections extracted from the volume. For patch classification, each voxel is classified from locally extracted raw data of three orthogonal planes centered on it. We propose a bootstrap resampling approach to enhance the training in our highly imbalanced data. For semantic segmentation, parts of a needle are detected in cross-sections perpendicular to the lateral and elevational axes. We propose to exploit the structural information in the data with a novel thick-slice processing approach for efficient modeling of the context. RESULTS: Our introduced methods successfully detect 17 and 22 G needles with a single trained network, showing a robust generalized approach. Extensive ex-vivo evaluations on datasets of chicken breast and porcine leg show 80 and 84% F1-scores, respectively. Furthermore, very short needles are detected with tip localization errors of less than 0.7 mm for lengths of only 5 and 10 mm at 0.2 and 0.36 mm voxel sizes, respectively. CONCLUSION: Our method is able to accurately detect even very short needles, ensuring that the needle and its tip are maximally visible in the visualized plane during the entire intervention, thereby eliminating the need for advanced bi-manual coordination of the needle and transducer.

CASPER: computer-aided segmentation of imperceptible motion-a learning-based tracking of an invisible needle in ultrasound.

PURPOSE: This paper presents a new micro-motion-based approach to track a needle in ultrasound images captured by a handheld transducer. METHODS: We propose a novel learning-based framework to track a handheld needle by detecting microscale variations of motion dynamics over time. The current state of the art on using motion analysis for needle detection uses absolute motion and hence work well only when the transducer is static. We have introduced and evaluated novel spatiotemporal and spectral features, obtained from the phase image, in a self-supervised tracking framework to improve the detection accuracy in the subsequent frames using incremental training. Our proposed tracking method involves volumetric feature selection and differential flow analysis to incorporate the neighboring pixels and mitigate the effects of the subtle tremor motion of a handheld transducer. To evaluate the detection accuracy, the method is tested on porcine tissue in-vivo, during the needle insertion in the biceps femoris muscle. RESULTS: Experimental results show the mean, standard deviation and root-mean-square errors of [Formula: see text], [Formula: see text] and [Formula: see text] in the insertion angle, and 0.82, 1.21, 1.47 mm, in the needle tip, respectively. CONCLUSIONS: Compared to the appearance-based detection approaches, the proposed method is especially suitable for needles with ultrasonic characteristics that are imperceptible in the static image and to the naked eye.

Medical Instrument Detection in 3-Dimensional Ultrasound Data Volumes.

Ultrasound-guided medical interventions are broadly applied in diagnostics and therapy, e.g., regional anesthesia or ablation. A guided intervention using 2-D ultrasound is challenging due to the poor instrument visibility, limited field of view, and the multi-fold coordination of the medical instrument and ultrasound plane. Recent 3-D ultrasound transducers can improve the quality of the image-guided intervention if an automated detection of the needle is used. In this paper, we present a novel method for detecting medical instruments in 3-D ultrasound data that is solely based on image processing techniques and validated on various ex vivo and in vivo data sets. In the proposed procedure, the physician is placing the 3-D transducer at the desired position, and the image processing will automatically detect the best instrument view, so that the physician can entirely focus on the intervention. Our method is based on the classification of instrument voxels using volumetric structure directions and robust approximation of the primary tool axis. A novel normalization method is proposed for the shape and intensity consistency of instruments to improve the detection. Moreover, a novel 3-D Gabor wavelet transformation is introduced and optimally designed for revealing the instrument voxels in the volume, while remaining generic to several medical instruments and transducer types. Experiments on diverse data sets, including in vivo data from patients, show that for a given transducer and an instrument type, high detection accuracies are achieved with position errors smaller than the instrument diameter in the 0.5-1.5-mm range on average.

Detection of an invisible needle in ultrasound using a probabilistic SVM and time-domain features.

We propose a novel learning-based approach to detect an imperceptible hand-held needle in ultrasound images using the natural tremor motion. The minute tremor induced on the needle however is also transferred to the tissue in contact with the needle, making the accurate needle detection a challenging task. The proposed learning-based framework is based on temporal analysis of the phase variations of pixels to classify them according to the motion characteristics. In addition to the classification, we also obtain a probability map of the segmented pixels by cross-validation. A Hough transform is then used on the probability map to localize the needle using the segmented needle and posterior probability estimate. The two-step probability-weighted localization on the segmented needle in a learning framework is the key innovation which results in localization improvement and adaptability to specific clinical applications. The method was tested in vivo for a standard 17 gauge needle inserted at 50-80° insertion angles and 40-60mm depths. The results showed an average accuracy of (2.12°, 1.69mm) and 81%±4% for localization and classification, respectively.

Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling.

PURPOSE: This paper presents a new approach to detect a standard handheld needle in ultrasound-guided interventions. METHODS: Our proposal is to use natural hand tremor, which causes minute displacement of the needle, to detect the needle in ultrasound B-mode images. Subtle displacements arising from tremor motion have a periodic pattern which is usually imperceptible to the naked eye in the B-mode image. We use these displacement measurements in a spatiotemporal framework to detect linear structures with periodic pattern among a sequence of frames. The needle trajectory is estimated as a linear path in the image having maximum spectral correlation with the time trace of displacement due to tremor. A coarse estimation process is followed by a fine estimation step, where the motion pattern is analyzed along spatiotemporal linear paths with various angles originating from the estimated puncture site, within the trajectory channel. Spectral coherency is derived for each sample path versus the reference path, and the needle trajectory is identified as the mean of the sample paths with the maximum coherence within the tremor frequency range. RESULTS: To evaluate the detection accuracy, we tested the method in vivo on porcine tissue, where the needle was inserted into the biceps femoris muscle. To understand whether tremor itself affects needle position, the maximum angular change due to tremor was calculated: mean, standard deviation (SD) and root-mean-square (RMS) measurement of [Formula: see text] and [Formula: see text]. The accuracy of the needle trajectory was calculated by comparing to an expert manual segmentation, averaged over the captured data and presented in mean, SD and RMS error of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: Results demonstrate that natural tremor motion creates minute coherent motion along the needle, which could be used to localize the needle trajectory within the acceptable accuracy. This method is suitable for standard needles used clinically.

Expectation and cooperation in prisoner's dilemmas: The moderating role of game riskiness.

This paper investigated the effect of risk orientation, game riskiness, and expectation of cooperation on cooperation in one-shot prisoner's dilemmas (PD). Participants in pairs played PD games that varied on game riskiness such that for half of the games cooperation was more risky than defection (more risky games) while for another half cooperation was less risky (less risky games). They estimated how likely it was that the other player was going to cooperate (expectation of cooperation) before they made their cooperation/defection decision on each game. Supporting the Goal/Expectation Hypothesis, we replicated the effect that expectation of cooperation enhanced cooperation. We also found that risk-seeking individuals cooperated more in more risky games whereas risk-averse individuals cooperated more in less risky games. More importantly, we found that game riskiness moderated the effect of expectation of cooperation on cooperation. The positive effect of expectation of cooperation on cooperation was stronger for more risky games than for less risky games. Our results illustrated how the relation between expectation and cooperation as stipulated by the Goal/Expectation Hypothesis was moderated by riskiness of the situations.

Needle Trajectory and Tip Localization in Real-Time 3-D Ultrasound Using a Moving Stylus.

Described here is a novel approach to needle localization in 3-D ultrasound based on automatic detection of small changes in appearance on movement of the needle stylus. By stylus oscillation, including its full insertion into the cannula to the tip, the image processing techniques can localize the needle trajectory and the tip in the 3-D ultrasound volume. The 3-D needle localization task is reduced to two 2-D localizations using orthogonal projections. To evaluate our method, we tested it on three different ex vivo tissue types, and the preliminary results indicated that the method accuracy lies within clinical acceptance, with average error ranges of 0.9°-1.4° in needle trajectory and 0.8-1.1 mm in needle tip. Results also indicate that method performance is independent of the echogenicity of the tissue. This technique is a safe way of producing ultrasonic intensity changes and appears to introduce negligible risk to the patient, as the outer cannula remains fixed.

Operationalising elaboration theory for simulation instruction design: a Delphi study.

OBJECTIVE: The aim of this study was to assess the feasibility of incorporating the Delphi process within the simplifying conditions method (SCM) described in elaboration theory (ET) to identify conditions impacting the complexity of procedural skills for novice learners. METHODS: We generated an initial list of conditions impacting the complexity of lumbar puncture (LP) from key informant interviews (n = 5) and a literature review. Eighteen clinician-educators from six different medical specialties were subsequently recruited as expert panellists. Over three Delphi rounds, these panellists rated: (i) their agreement with the inclusion of the simple version of the conditions in a representative ('epitome') training scenario, and (ii) how much the inverse (complex) version increases LP complexity for a novice. Cronbach's α-values were used to assess inter-rater agreement. RESULTS: All panellists completed Rounds 1 and 2 of the survey and 17 completed Round 3. In Round 1, Cronbach's α-values were 0.89 and 0.94 for conditions that simplify and increase LP complexity, respectively; both values increased to 0.98 in Rounds 2 and 3. With the exception of 'high CSF (cerebral spinal fluid) pressure', panellists agreed with the inclusion of all conditions in the simplest (epitome) training scenario. Panellists rated patient movement, spinal anatomy, patient cooperativeness, body habitus, and the presence or absence of an experienced assistant as having the greatest impact on the complexity of LP. CONCLUSIONS: This study demonstrated the feasibility of using expert consensus to establish conditions impacting the complexity of procedural skills, and the benefits of incorporating the Delphi method into the SCM. These data can be used to develop and sequence simulation scenarios in a progressively challenging manner. If the theorised learning gains associated with ET are realised, the methods described in this study may be applied to the design of simulation training for other procedural and non-procedural skills, thereby advancing the agenda of theoretically based instruction design in health care simulation.

Enhanced needle localization in ultrasound using beam steering and learning-based segmentation.

Segmentation of needles in ultrasound images remains a challenging problem. In this paper, we introduce a machine learning-based method for needle segmentation in 2D beam-steered ultrasound images. We used a statistical boosting approach to train a pixel-wise classifier for needle segmentation. The Radon transform was then used to find the needle position and orientation from the segmented image. We validated our method with data from ex vivo specimens and clinical nerve block procedures, and compared the results to those obtained using previously reported needle segmentation methods. Results show improved localization success and accuracy using the proposed method. For the ex vivo datasets, assuming that the needle orientation was known a priori, the needle was successfully localized in 86.2% of the images, with a mean targeting error of 0.48mm. The robustness of the proposed method to a lack of a priori knowledge of needle orientation was also demonstrated. For the clinical datasets, assuming that the needle orientation was closely aligned with the beam steering angle selected by the physician, the needle was successfully localized in 99.8% of the images, with a mean targeting error 0.19mm. These results indicate that the learning-based segmentation method may allow for increased targeting accuracy and enhanced visualization during ultrasound-guided needle procedures.

Transmembrane and coiled-coil domain family 1 is a novel protein of the endoplasmic reticulum.

The endoplasmic reticulum (ER) is a continuous membrane network in eukaryotic cells comprising the nuclear envelope, the rough ER, and the smooth ER. The ER has multiple critical functions and a characteristic structure. In this study, we identified a new protein of the ER, TMCC1 (transmembrane and coiled-coil domain family 1). The TMCC family consists of at least 3 putative proteins (TMCC1-3) that are conserved from nematode to human. We show that TMCC1 is an ER protein that is expressed in diverse human cell lines. TMCC1 contains 2 adjacent transmembrane domains near the C-terminus, in addition to coiled-coil domains. TMCC1 was targeted to the rough ER through the transmembrane domains, whereas the N-terminal region and C-terminal tail of TMCC1 were found to reside in the cytoplasm. Moreover, the cytosolic region of TMCC1 formed homo- or hetero-dimers or oligomers with other TMCC proteins and interacted with ribosomal proteins. Notably, overexpression of TMCC1 or its transmembrane domains caused defects in ER morphology. Our results suggest roles of TMCC1 in ER organization.

Z-DOC: a serious game for Z-plasty procedure training.

We present Z-DOC, a (prototype) serious game for training plastic surgery residents the steps comprising the Z-plasty surgical procedure. Z-DOC employs touch-based interactions and promotes competition amongst multiple players/users thus promote engagement and motivation. It is hypothesized that by learning the Z-plasty procedure in an interactive, engaging, and fun gaming environment, trainees will have a much better understanding of the procedure than by traditional learning modalities.

Stability of pain-related medications, metabolites, and illicit substances in urine.

BACKGROUND: Effective urine drug testing requires an understanding of the stability of medications, metabolites and other substances excreted in the urine matrix. When the testing results do not fit the clinical picture, physicians frequently request repeat testing of the original specimen in order to corroborate the results. We determined the stability in urine of various medications, metabolites, and illicit substances commonly requested for testing by physicians treating patients with pain and pain-related disorders. METHODS: Quantitative analyses of urine specimens were performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Two replicates at a high and low concentration were analyzed at time 0, and after 2, 3 and 6 months following storage at +4 °C and -20 °C. At each time interval, the percent difference from time 0 for each analyte was calculated and averaged for each storage condition. RESULTS: For the majority of medications, the percent differences were within 20% of the original measurement for all 3 storage conditions. All were within 30% of the original measurement after 2, 3 and 6 months in all storage conditions, except for 7-amino-clonazepam, and carboxy-tetrahydrocannabinol. CONCLUSIONS: The findings from the current study confirm that the majority of medications, metabolites, and illicit substances commonly requested for testing by physicians treating patients with pain and pain-related disorders are stable within 20% of the original concentration when stored refrigerated or frozen for up to 6 months. Thus, delayed testing, repeat testing, and add-on testing of urine specimens can yield reliable results for up to 6 months following the urine collection date.