Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, outperforming existing state-of-the-art approaches that leverage multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

Curated and Annotated Dataset of Lung US Images in Zambian Children with Clinical Pneumonia

See also the commentary by Sitek in this issue. Supplemental material is available for this article.

Consistent long-term practice leads to consistent improvement: Benefits of self-managed therapy for language and cognitive deficits using a digital therapeutic

Background

Although speech-language therapy (SLT) is proven to be beneficial to recovery of post-stroke aphasia, delivering sufficiently high amounts of dosage remains a problem in real-world clinical practice. Self-managed SLT was introduced to solve the problem. Previous research showed in a 10-week period, increased dosage frequency could lead to better performance, however, it is uncertain if dosage still affects performance over a longer period of practice time and whether gains can be seen following practice over several months.

Objective

This study aims to evaluate data from a health app (Constant Therapy) to investigate the relationship between dosage amount and improvements following a 30-week treatment period. Two cohorts of users were analyzed. One was comprised of patients with a consistent average weekly dosage amount and the other cohort was comprised of users whose practice had higher variability.

Methods

We conducted two analyses with two cohorts of post-stroke patients who used Constant Therapy. The first cohort contains 537 "consistent" users, while the second cohort contains 2,159. The 30-week practice period was split into three consecutive 10-week practice windows to calculate average dosage amount. In each 10-week practice period, patients were grouped by their average dosage into low (0-15 min/week), medium (15-40 min/week) and moderate dosage (greater than 40 min/week) groups. Linear mixed-effects models were employed to evaluate if dosage amount was a significant factor affecting performance. Pairwise comparison was also applied to evaluate the slope difference between groups.

Results

For the consistent cohort, medium (β = .002, t _17,700 = 7.64, P < .001) and moderate (β = .003, t _9,297 = 7.94, P < .001) dosage groups showed significant improvement compared to the low dosage group. The moderate group also showed greater improvement compared to the medium group. For the variable cohort in analysis 2, the same trend was shown in the first two 10-week windows, however, in weeks 21-30, the difference was insignificant between low and medium groups (β = .001, t = 1.76, P = .078).

Conclusions

This study showed a higher dosage amount is related to greater therapy outcomes in over 6 months of digital self-managed therapy. It also showed that regardless of the exact pattern of practice, self-managed SLT leads to significant and sustained performance gains.

A Graph-Transformer for Whole Slide Image Classification

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected 4,818 WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy = 91.2 ± 2.5%) based on five-fold cross-validation, and mean accuracy = 82.3 ± 1.0% on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia

BACKGROUND

Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke.

METHODS

Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data.

RESULTS

The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68-0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87).

CONCLUSIONS

While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

The impact of ear growth on identification rates using an ear biometric system in young infants

Background Accurate patient identification is essential for delivering longitudinal care. Our team developed an ear biometric system (SEARCH) to improve patient identification. To address how ear growth affects matching rates longitudinally, we constructed an infant cohort, obtaining ear image sets monthly to map a 9-month span of observations. This analysis had three main objectives: 1) map trajectory of ear growth during the first 9 months of life; 2) determine the impact of ear growth on matching accuracy; and 3) explore computer vision techniques to counter a loss of accuracy.   Methodology Infants were enrolled from an urban clinic in Lusaka, Zambia. Roughly half were enrolled at their first vaccination visit and ~half at their last vaccination. Follow-up visits for each patient occurred monthly for 6 months. At each visit, we collected four images of the infant’s ears, and the child’s weight. We analyze ear area versus age and change in ear area versus age. We conduct pair-wise comparisons for all age intervals. Results From 227 enrolled infants we acquired age-specific datasets for 6 days through 9 months. Maximal ear growth occurred between 6 days and 14 weeks. Growth was significant until 6 months of age, after which further growth appeared minimal. Examining look-back performance to the 6-month visit, baseline pair-wise comparisons yielded identification rates that ranged 46.9–75%. Concatenating left and right ears per participant improved identification rates to 61.5–100%. Concatenating images captured on adjacent visits further improved identification rates to 90.3–100%. Lastly, combining these two approaches improved identification to 100%. All matching strategies showed the weakest matching rates during periods of maximal growth (i.e., <6 months). Conclusion By quantifying the effect that ear growth has on performance of the SEARCH platform, we show that ear identification is a feasible solution for patient identification in an infant population 6 months and above.

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia

Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification. Unlike many popular biometric approaches (e.g., fingerprints, irises, facial recognition), ears are noninvasive and easily accessible on individuals across a lifespan. Our ear biometric tool uses a combination of hardware and software to identify a person using an image of their ear. The hardware supports an image capturing process that reduces undesired variability. The software uses a pattern recognition algorithm to transform an image of the ear into a unique identifier. We created three cross-sectional datasets of ear images, each increasing in complexity, with the final dataset representing our target use-case population of Zambian infants (N=224, aged 6days-6months). Using these datasets, we conducted a series of validation experiments, which informed iterative improvements to the system. Results of the improved system, which yielded high recognition rates across the three datasets, demonstrate the feasibility of an Android ear biometric tool as a solution to the persisting patient identification challenge.

Text and metadata extraction from scanned Arabic documents using support vector machines

Text information in scanned documents becomes accessible only when extracted and interpreted by a text recognizer. For a recognizer to work successfully, it must have detailed location information about the regions of the document images that it is asked to analyse. It will need focus on page regions with text skipping non-text regions that include illustrations or photographs. However, text recognizers do not work as logical analyzers. Logical layout analysis automatically determines the function of a document text region, that is, it labels each region as a title, paragraph, or caption, and so on, and thus is an essential part of a document understanding system. In the past, rule-based algorithms have been used to conduct logical layout analysis, using limited size data sets. We here instead focus on supervised learning methods for logical layout analysis. We describe LABA, a system based on multiple support vector machines to perform logical Layout Analysis of scanned Books pages in Arabic. The system detects the function of a text region based on the analysis of various images features and a voting mechanism. For a baseline comparison, we implemented an older but state-of-the-art neural network method. We evaluated LABA using a data set of scanned pages from illustrated Arabic books and obtained high recall and precision values. We also found that the F-measure of LABA is higher for five of the tested six classes compared to the state-of-the-art method.

Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity. The subset of obstacles provided to the algorithm was determined by the animal's sensing range in terms of metric and topological distance. The algorithmic calculated velocities showed good agreement with observed biological velocities, indicating that the algorithm was an informative basis for comparison with the three species and could potentially be improved for engineered applications with further study.

Perceptual Modalities Guiding Bat Flight in a Native Habitat

Flying animals accomplish high-speed navigation through fields of obstacles using a suite of sensory modalities that blend spatial memory with input from vision, tactile sensing, and, in the case of most bats and some other animals, echolocation. Although a good deal of previous research has been focused on the role of individual modes of sensing in animal locomotion, our understanding of sensory integration and the interplay among modalities is still meager. To understand how bats integrate sensory input from echolocation, vision, and spatial memory, we conducted an experiment in which bats flying in their natural habitat were challenged over the course of several evening emergences with a novel obstacle placed in their flight path. Our analysis of reconstructed flight data suggests that vision, echolocation, and spatial memory together with the possible exercise of an ability in using predictive navigation are mutually reinforcing aspects of a composite perceptual system that guides flight. Together with the recent development in robotics, our paper points to the possible interpretation that while each stream of sensory information plays an important role in bat navigation, it is the emergent effects of combining modalities that enable bats to fly through complex spaces.

A protocol and calibration method for accurate multi-camera field videography

Stereo videography is a powerful technique for quantifying the kinematics and behavior of animals, but it can be challenging to use in an outdoor field setting. We here present a workflow and associated software for performing calibration of cameras placed in a field setting and estimating the accuracy of the resulting stereoscopic reconstructions. We demonstrate the workflow through example stereoscopic reconstructions of bat and bird flight. We provide software tools for planning experiments and processing the resulting calibrations that other researchers may use to calibrate their own cameras. Our field protocol can be deployed in a single afternoon, requiring only short video clips of light, portable calibration objects.

The Kernel Semi–Least Squares Method for Sparse Distance Approximation

We extend the semi–least squares problem defined by Rao and Mitra ( 1971 ) to the kernel semi–least squares problem. We introduce subset projection, a technique that produces a solution to this problem. We show how the results of subset projection can be used to approximate a computationally expensive distance metric.

Explaining clustered ventilation defects via a minimal number of airway closure locations

Computational models of the human lung have been developed to study lung physiology and have been used to identify the airways responsible for mechanical dysfunction in asthmatics. Tgavalekos et al. used models anatomically consistent with the human lung to link ventilation defects to the heterogeneous closure of small airways. Their approach implicitly assumed a high degree of independence between airway closures as indicated by the low compactness of the airway structures mapped to individual ventilation defects. Venegas et al. however, have found that significant mutual dependence of airways may play a role in patchy ventilation of asthmatics. This led us to explore the question to what extent anatomically consistent models can be built which do not implicitly assume high independence of airways but instead allow for the mutual dependence of airways responsible for ventilation defects. We propose an algorithm for generating subject-specific airway-tree models that minimize the number of airways that must be closed or severely constricted to cause observed ventilation defects. We also propose novel approaches for measuring the compactness of airway structures. Our approach shows that anatomically consistent models which link compact airway structures to ventilation defects can be built. Our model also shows that some ventilation defects may be caused by closures of larger airways than previously reported.

Brazilian free-tailed bats as insect pest regulators in transgenic and conventional cotton crops

During the past 12000 years agricultural systems have transitioned from natural habitats to conventional agricultural regions and recently to large areas of genetically engineered (GE) croplands. This GE revolution occurred for cotton in a span of slightly more than a decade during which a switch occurred in major cotton production areas from growing 100% conventional cotton to an environment in which 95% transgenics are grown. Ecological interactions between GE targeted insects and other insectivorous insects have been investigated. However, the relationships between ecological functions (such as herbivory and ecosystem transport) and agronomic benefits of avian or mammalian insectivores in the transgenic environment generally remain unclear, although the importance of some agricultural pest management services provided by insectivorous species such as the Brazilian free-tailed bat, Tadarida brasiliensis, have been recognized. We developed a dynamic model to predict regional-scale ecological functions in agricultural food webs by using the indicators of insect pest herbivory measured by cotton boll damage and insect emigration from cotton. In the south-central Texas Winter Garden agricultural region we find that the process of insectivory by bats has a considerable impact on both the ecology and valuation of harvest in Bacillus thuringiensis (Bt) transgenic and nontransgenic cotton crops. Predation on agricultural pests by insectivorous bats may enhance the economic value of agricultural systems by reducing the frequency of required spraying and delaying the ultimate need for new pesticides. In the Winter Garden region, the presence of large numbers of insectivorous bats yields a regional summer dispersion of adult pest insects from Bt cotton that is considerably reduced from the moth emigration when bats are absent in either transgenic or non-transgenic crops. This regional decrease of pest numbers impacts insect herbivory on a transcontinental scale. With a few exceptions, we find that the agronomics of both Bt and conventional cotton production is more profitable when large numbers of insectivorous bats are present.

Detecting objects of variable shape structure with hidden state shape models

This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude, and produces highly accurate results in experiments on non-rigid hand motion tracking.

Pulmonary fissure segmentation on CT

A pulmonary fissure is a boundary between the lobes in the lungs. Its segmentation is of clinical interest as it facilitates the assessment of lung disease on a lobar level. This paper describes a new approach for segmenting the major fissures in both lungs on thin-section computed tomography (CT). An image transformation called "ridge map" is proposed for enhancing the appearance of fissures on CT. A curve-growing process, modeled by a Bayesian network, is described that is influenced by both the features of the ridge map and prior knowledge of the shape of the fissure. The process is implemented in an adaptive regularization framework that balances these influences and reflects the causal dependencies in the Bayesian network using an entropy measure. The method effectively alleviates the problem of inappropriate weights of regularization terms, an effect that can occur with static regularization methods. The method was applied to segment and visualize the lobes of the lungs on chest CT of 10 patients with pulmonary nodules. Only 78 out of 3286 left or right lung regions with fissures (2.4%) required manual correction. The average distance between the automatically segmented and the manually delineated "ground-truth" fissures was 1.01 mm, which was similar to the average distance of 1.03 mm between two sets of manually segmented fissures. The method has a linear-time worst-case complexity and segments the upper lung from the lower lung on a standard computer in less than 5 min.

The correlation between internal and external markers for abdominal tumors: Implications for respiratory gating

PURPOSE

The correlation of the respiratory motion of external patient markers and abdominal tumors was examined. Data of this type are important for image-guided therapy techniques, such as respiratory gating, that monitor the movement of external fiducials.

METHODS AND MATERIALS

Fluoroscopy sessions for 4 patients with internal, radiopaque tumor fiducial clips were analyzed by computer vision techniques. The motion of the internal clips and the external markers placed on the patient's abdominal skin surface were quantified and correlated.

RESULTS

In general, the motion of the tumor and external markers were well correlated. The maximum amount of peak-to-peak craniocaudal tumor motion was 2.5 cm. The ratio of tumor motion to external-marker motion ranged from 0.85 to 7.1. The variation in tumor position for a given external-marker position ranged from 2 to 9 mm. The period of the breathing cycle ranged from 2.7 to 4.5 seconds, and the frequency patterns for both the tumor and the external markers were similar.

CONCLUSIONS

Although tumor motion generally correlated well with external fiducial marker motion, relatively large underlying tumor motion can occur compared with external-marker motion and variations in the tumor position for a given marker position. Treatment margins should be determined on the basis of a detailed understanding of tumor motion, as opposed to relying only on external-marker information.

Segmentation of nodules on chest computed tomography for growth assessment

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.

Quantification of respiration-induced abdominal tumor motion and its impact on IMRT dose distributions

PURPOSE

The treatment of moving targets with intensity-modulated radiotherapy may introduce errors in dose delivery. The motion of tumors in the abdomen was studied using quantitative fluoroscopic analysis, and the effect on dose delivery to the target was studied.

METHODS AND MATERIALS

Fluoroscopy sessions for 7 patients with pancreas or liver tumors and fiducial clips were recorded, converted to digital format, and analyzed to quantify the characteristics of tumor motion. Intensity-modulated radiotherapy plans were generated for 3 patients (a total of five plans), and the dose-volume histograms for the target volume were compared between plans with and without tumor motion.

RESULTS

The average magnitude of the peak-to-peak motion for the 7 patients in the craniocaudal and AP directions was 7.4 mm and 3.8 mm, respectively. The clip motion varied widely, because the maximal clip excursions were about 47% greater than the average clip excursions for each patient. The inclusion of tumor motion did not lead to a significant degradation in the target dose-volume histogram for four of five treatment plans studied.

CONCLUSION

The amount of tumor motion for most patients in this study was not large but could, in some instances, significantly degrade the planned target dose-volume histogram. For some patients, therefore, motion mitigation or intervention during treatment may be necessary.

Small pulmonary nodules: volume measurement at chest CT--phantom study

Three-dimensional methods for quantifying pulmonary nodule volume at computed tomography (CT) and the effect of imaging variables were studied by using a realistic phantom. Two fixed-threshold methods, a partial-volume method (PVM) and a variable method, were used to calculate volumes of 40 plastic nodules (largest dimension, <5 mm: 20 nodules with solid attenuation and 20 with ground-glass attenuation) of known volume. Tube current times (20 and 120 mAs), reconstruction algorithms (high and low frequency), and nodule characteristics were studied. Higher precision was associated with use of a PVM with predetermined pure nodule attenuation, high-frequency algorithm, and diagnostic CT technique (120 mAs). A PVM is promising for volume quantification and follow-up of nodules.

Landmark detection in the chest and registration of lung surfaces with an application to nodule registration

We developed an automated system for registering computed tomography (CT) images of the chest temporally. Our system detects anatomical landmarks, in particular, the trachea, sternum and spine, using an attenuation-based template matching approach. It computes the optimal rigid-body transformation that aligns the corresponding landmarks in two CT scans of the same patient. This transformation then provides an initial registration of the lung surfaces segmented from the two scans. The initial surface alignment is refined step by step in an iterative closest-point (ICP) process. To establish the correspondence of lung surface points, Elias' nearest neighbor algorithm was adopted. Our method improves the processing time of the original ICP algorithm from O(kn log n) to O(kn), where k is the number of iterations and n the number of surface points. The surface transformation is applied to align nodules in the initial CT scan with nodules in the follow-up scan. For 56 out of 58 nodules in the initial CT scans of 10 patients, nodule correspondences in the follow-up scans are established correctly. Our methods can therefore potentially facilitate the radiologist's evaluation of pulmonary nodules on chest CT for interval growth.

The camera mouse: visual tracking of body features to provide computer access for people with severe disabilities

The "Camera Mouse" system has been developed to provide computer access for people with severe disabilities. The system tracks the computer user's movements with a video camera and translates them into the movements of the mouse pointer on the screen. Body features such as the tip of the user's nose or finger can be tracked. The visual tracking algorithm is based on cropping an online template of the tracked feature from the current image frame and testing where this template correlates in the subsequent frame. The location of the highest correlation is interpreted as the new location of the feature in the subsequent frame. Various body features are examined for tracking robustness and user convenience. A group of 20 people without disabilities tested the Camera Mouse and quickly learned how to use it to spell out messages or play games. Twelve people with severe cerebral palsy or traumatic brain injury have tried the system, nine of whom have shown success. They interacted with their environment by spelling out messages and exploring the Internet.

Chest CT: automated nodule detection and assessment of change over time--preliminary experience

The authors developed a computer system that automatically identifies nodules at chest computed tomography, quantifies their diameter, and assesses for change in size at follow-up. The automated nodule detection system identified 318 (86%) of 370 nodules in 16 studies (eight initial and eight follow-up studies) obtained in eight oncology patients with known nodules. Assessment of change in nodule size by the computer matched that by the thoracic radiologist (Spearman rank correlation coefficient, 0.932).

Gastric carcinoma metastatic to the site of a congenital melanocytic nevus

Cutaneous metastasis from gastric carcinoma is uncommon. We describe a patient with a metastasis from gastric carcinoma to a congenital melanocytic nevus. The diagnosis was confirmed by positive immunohistochemical staining for cytokeratin 20 and lack of cytokeratin 7.

Myoepithelial differentiation in benign sweat gland tumors. Demonstrated by a monoclonal antibody to alpha-smooth muscle actin

One hundred and two cases of benign sweat gland tumors of the skin were studied for the presence of myoepithelial cells specifically identified by a monoclonal antibody to alpha-smooth muscle actin on paraffin-embedded tissues. The monoclonal antibody gave a positive result in 12 of 12 cases of cylindroma, 14 of 16 cases of spiradenoma, 2 of 2 cases of apocrine tubular adenoma (papillary eccrine adenoma), 5 of 5 cases of apocrine hidrocystoma, 5 of 5 cases of hidradenoma papilliferum, and in 10 of 10 cases of syringocystadenoma papilliferum. Rare myoepithelial cells were detected in only 1 of 10 cases of mixed tumor, apocrine type. There was no immunoreactivity for alpha-smooth muscle actin in eccrine hidrocystoma (2 cases), mixed tumor of eccrine type (2 cases), syringoma (7 cases), hidroacanthoma simplex (1 case), eccrine poroma (14 cases), clear cell hidradenoma (15 cases), and in 1 case of eccrine syringofibroadenoma. Our data support the concept that myoepithelial cells are seen in most sweat gland tumors considered to differentiate toward the secretory coil of the normal sweat gland. In contrast, myoepithelial cells are absent in tumors showing differentiation toward the excretory (ductal) component of the gland.

[Arndt-Gottron scleromyxedema. Case report and review of therapeutic possibilities]

We report on a 54-year-old patient with an 11-year history of Arndt-Gottron scleromyxoedema. We found typical lichenoid papules, diffuse skin thickening and skin hardening. Clinical and laboratory investigations revealed a monoclonal gammopathy of the IgG1 type, normal histology of the bone marrow and normal urine. Therefore, a diagnosis of monoclonal gammopathy of undetermined significance was recorded. Phimosis and stenosis of the urethra were the only other pathologic findings. Systemic treatment with chlorambucil and PUVA had little beneficial effect on the skin thickening after 9 months.

[Eccrine poroma. A clinico-pathologic and immunohistologic study with special reference to tumor cell differentiation]

In this study 15 eccrine poromas were analysed clinically, histologically and immunohistologically. They were all solitary lesions, showing a predilection for the head and neck. In none of the tumours was diagnosis possible on the basis of clinical examination. Histomorphologically, eccrine poromas were characterized by aggregations of neoplastic cells continuous with the epidermis. The neoplasms consisted of two cell types, poroid and cuticular. Poroid cells predominated, while cuticular cells were only found in small foci, sometimes showing tubular differentiation. Immunohistologically, most of the tumour cells showed a cytokeratin pattern (CK1, 5, 10, 11+, CK1-19+) favouring differentiation toward the abluminal cell of the dermal eccrine duct rather than toward the abluminal cell of the intraepidermal segment of the eccrine duct. Only a small proportion of cells revealed the immunohistological features of the abluminal cell of the intraepidermal duct (CK1+, CK1, 5, 10, 11+, CK1-19+). In addition, cuticular cells showed differentiation toward the luminal cell of the eccrine duct (CK19+, CK1, 5, 10, 11+, CK1-19+). Simple-type cytokeratins such as CK7 and CK18 were not expressed. In conclusion, our findings favour the hypothesis that ascribes the origin of eccrine poroma to a pluripotential stem cell of the transitional zone between the dermal and the intraepidermal segments of the eccrine duct.

[Viscerocutaneous hemangiomatosis--the so-called blue-rubber-bleb nevus syndrome]

A case of so-called blue rubber bleb naevus syndrome in a 17-year-old girl is presented. Apart from multiple haemangiomas of the skin, further lesions were present in the small and large intestine, which were demonstrated by endoscopy. The features characterizing the syndrome as a separate entity within the group of angiomatous diseases are discussed.

[Lichen aureus zosteriformis]

A case of lichen aureus is presented, which is atypical in that a very large area was involved and there was a good response to topical fluorinated steroid therapy. Similarities to other cases reported in the literature are demonstrated.