Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

BACKGROUND

With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.

METHODS

To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen. They comprise the amount of blood and smoke in the surgical field, six instruments, and two anatomic structures. Annotation of selected frames from robot-assisted minimally invasive esophagectomies was performed by at least three independent medical experts. To test whether AL reduces annotation effort, we performed a prospective annotation study comparing AL with equidistant sampling (EQS) for frame selection. Multiple Bayesian ResNet18 architectures were trained on a multicentric dataset, consisting of 22 videos from two centers.

RESULTS

In total, 14,004 frames were tag annotated. A mean F1-score of 0.75 ± 0.16 was achieved for all features. The highest F1-score was achieved for the instruments (mean 0.80 ± 0.17). This result is also reflected in the inter-rater-agreement (1-rater-kappa > 0.82). Compared to EQS, AL showed better recognition results for the instruments with a significant difference in the McNemar test comparing correctness of predictions. Moreover, in contrast to EQS, AL selected more frames of the four less common instruments (1512 vs. 607 frames) and achieved higher F1-scores for common instruments while requiring less training frames.

CONCLUSION

We presented ten surgomic features relevant for bleeding events in esophageal surgery automatically extracted from surgical video using ML. AL showed the potential to reduce annotation effort while keeping ML performance high for selected features. The source code and the trained models are published open source.

Sources of performance variability in deep learning-based polyp detection

PURPOSE

Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening.

METHODS

Our contribution is twofold. Firstly, we present the winning solution of the Endoscopy Computer Vision Challenge on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices.

RESULTS

Based on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance.

CONCLUSION

We conclude from our study that (1) performance results in polyp detection are highly sensitive to various design choices, (2) common metric configurations do not reflect the clinical need and rely on suboptimal hyperparameters and (3) comparison of performance across datasets can be largely misleading. Our work could be a first step towards reconsidering common validation strategies in deep learning-based colonoscopy and beyond.

Labelling instructions matter in biomedical image analysis

Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labelling instructions are key. Despite their importance, their optimization remains largely unexplored. Here we present a systematic study of labelling instructions and their impact on annotation quality in the field. Through comprehensive examination of professional practice and international competitions registered at the Medical Image Computing and Computer Assisted Intervention Society, the largest international society in the biomedical imaging field, we uncovered a discrepancy between annotators’ needs for labelling instructions and their current quality and availability. On the basis of an analysis of 14,040 images annotated by 156 annotators from four professional annotation companies and 708 Amazon Mechanical Turk crowdworkers using instructions with different information density levels, we further found that including exemplary images substantially boosts annotation performance compared with text-only descriptions, while solely extending text descriptions does not. Finally, professional annotators constantly outperform Amazon Mechanical Turk crowdworkers. Our study raises awareness for the need of quality standards in biomedical image analysis labelling instructions.

Beyond rankings: Learning (more) from algorithm validation

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

PURPOSE

Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill.

METHODS

To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment.

RESULTS

F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team).

CONCLUSION

Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Ten years of image analysis and machine learning competitions in dementia

Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade: MIRIAD (2012), Alzheimer's Disease Big Data DREAM (2014), CADDementia (2014), Machine Learning Challenge (2014), MCI Neuroimaging (2017), TADPOLE (2017), and the Predictive Analytics Competition (2019). Based on two challenge evaluation frameworks, we analyzed how these grand challenges are complementing each other regarding research questions, datasets, validation approaches, results and impact. The seven grand challenges addressed questions related to screening, clinical status estimation, prediction and monitoring in (pre-clinical) dementia. There was little overlap in clinical questions, tasks and performance metrics. Whereas this aids providing insight on a broad range of questions, it also limits the validation of results across challenges. The validation process itself was mostly comparable between challenges, using similar methods for ensuring objective comparison, uncertainty estimation and statistical testing. In general, winning algorithms performed rigorous data pre-processing and combined a wide range of input features. Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used. To increase impact, future challenges could pay more attention to statistical analysis of which factors (i.e., features, models) relate to higher performance, to clinical questions beyond Alzheimer's disease, and to using testing data beyond the Alzheimer's Disease Neuroimaging Initiative. Grand challenges would be an ideal venue for assessing the generalizability of algorithm performance to unseen data of other cohorts. Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared. Given the potential and lessons learned in the past ten years, we are excited by the prospects of grand challenges in machine learning and neuroimaging for the next ten years and beyond.

Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence

With the impact of artificial intelligence (AI) algorithms on medical research on the rise, the importance of competitions for comparative validation of algorithms, so-called challenges, has been steadily increasing, to a point at which challenges can be considered major drivers of research, particularly in the biomedical image analysis domain. Given their importance, high quality, transparency, and interpretability of challenges is essential for good scientific practice and meaningful validation of AI algorithms, for instance towards clinical translation. This mini-review presents several issues related to the design, execution, and interpretation of challenges in the biomedical domain and provides best-practice recommendations. PATIENT SUMMARY: This paper presents recommendations on how to reliably compare the usefulness of new artificial intelligence methods for analysis of medical images.

Heidelberg colorectal data set for surgical data science in the sensor operating room

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.

Methods and open-source toolkit for analyzing and visualizing challenge results

Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.

BIAS: Transparent reporting of biomedical image analysis challenges

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit.

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.

Revision surgery for cervical artificial disc: Surgical technique and clinical results

OBJECTIVE

Cervical artificial disc replacement (C-ADR) was developed with the goal of preserving mobility of the cervical segment in patients with degenerative disc disease. So far, little is known about experiences with revision surgery and explantation of C-ADRs. Here, we report our experience with revision the third generation, Galileo-type disc prosthesis from a retrospective study of two institutions.

PATIENTS AND METHODS

Between November 2008 and July 2016, 16 patients with prior implantation of C-ADR underwent removal of the Galileo-type disc prosthesis (Signus, Medizintechnik, Germany) due to a call back by industry. In 10 patients C-ADR was replaced with an alternative prosthesis, 6 patients received an ACDF. Duration of surgery, time to revision, surgical procedure, complication rate, neurological status, histological findings and outcome were examined in two institutions.

RESULTS

The C-ADR was successfully revised in all patients. Surgery was performed through the same anterior approach as the initial access. Duration of the procedure varied between 43 and 80min. Access-related complications included irritation of the recurrent nerve in one patient and mal-positioning of the C-ADR in another patient. Follow up revealed two patients with permanent mild/moderate neurologic deficits, NDI (neck disability index) ranged between 10 and 42%.

CONCLUSIONS

Anterior exposure of the cervical spine for explantation and revision of C-ADR performed through the initial approach has an overall complication rate of 18.75%. Replacements of the Galileo-type disc prosthesis with an alternative prosthesis or conversion to ACDF are both suitable surgical options without significant difference in outcome.

Behavioral deficits in sepsis-surviving rats induced by cecal ligation and perforation

Sepsis and its complications are the leading causes of mortality in intensive care units, accounting for 10-50% of deaths. Intensive care unit survivors present long-term cognitive impairment, including alterations in memory, attention, concentration, and/or global loss of cognitive function. In the present study, we investigated behavioral alterations in sepsis-surviving rats. One hundred and ten male Wistar rats (3-4 months, 250-300 g) were submitted to cecal ligation and puncture (CLP), and 44 were submitted to sham operation. Forty-four rats (40%) survived after CLP, and all sham-operated animals survived and were used as control. Twenty animals of each group were used in the object recognition task (10 in short-term memory and 10 in long-term memory), 12 in the plus-maze test and 12 in the forced swimming test. Ten days after surgery, the animals were submitted individually to an object recognition task, plus-maze and forced swimming tests. A significant impairment of short- and long-term recognition memory was observed in the sepsis group (recognition index 0.75 vs 0.55 and 0.74 vs 0.51 for short- and long-term memory, respectively (P < 0.05). In the elevated plus-maze test no difference was observed between groups in any of the parameters assessed. In addition, sepsis survivors presented an increase in immobility time in the forced swimming test (180 vs 233 s, P < 0.05), suggesting the presence of depressive-like symptoms in these animals after recovery from sepsis. The present results demonstrated that rats surviving exposure to CLP, a classical sepsis model, presented recognition memory impairment and depressive-like symptoms but not anxiety-like behavior.

Differential role of entorhinal and hippocampal nerve growth factor in short- and long-term memory modulation

We studied the effects of infusion of nerve growth factor (NGF) into the hippocampus and entorhinal cortex of male Wistar rats (250-300 g, N = 11-13 per group) on inhibitory avoidance retention. In order to evaluate the modulation of entorhinal and hippocampal NGF in short- and long-term memory, animals were implanted with cannulae in the CA1 area of the dorsal hippocampus or entorhinal cortex and trained in one-trial step-down inhibitory avoidance (foot shock, 0.4 mA). Retention tests were carried out 1.5 h or 24 h after training to measure short- and long-term memory, respectively. Immediately after training, rats received 5 microl NGF (0.05, 0.5 or 5.0 ng) or saline per side into the CA1 area and entorhinal cortex. The correct position of the cannulae was confirmed by histological analysis. The highest dose of NGF (5.0 ng) into the hippocampus blocked short-term memory (P < 0.05), whereas the doses of 0.5 (P < 0.05) and 5.0 ng (P < 0.01) NGF enhanced long-term memory. NGF administration into the entorhinal cortex improved long-term memory at the dose of 5.0 ng (P < 0.05) and did not alter short-term memory. Taken as a whole, our results suggest a differential modulation by entorhinal and hippocampal NGF of short- and long-term memory.

Structure-Related Oxidative Damage in Rat Brain After Acute and Chronic Electroshock

The role of oxidative stress in electroconvulsive therapy-related effects is not well studied. The purpose of this study was to determine oxidative stress parameters in several brain structures after a single electroconvulsive seizure or multiple electroconvulsive seizures. Rats were given either a single electroconvulsive shock or a series of eight electroconvulsive shocks. Brain regions were isolated, and levels of oxidative stress in the brain tissue (cortex, hippocampus, striatum and cerebellum) were measured. We demonstrated a decrease in lipid peroxidation and protein carbonyls in the hippocampus, cerebellum, and striatum several times after a single electroconvulsive shock or multiple electroconvulsive shocks. In contrast, lipid peroxidation increases both after a single electroconvulsive shock or multiple electroconvulsive shocks in cortex. In conclusion, we demonstrate an increase in oxidative damage in cortex, in contrast to a reduction of oxidative damage in hippocampus, striatum, and cerebellum.

[An increased risk of kinesitherapy in silent myocardial ischemia?]

The effect of three-week standardized physical training on exercise-induced ischaemia was investigated in patients with silent ischaemia after myocardial infarction. 24-hour monitoring and exercise ECGs before and after the period of physical training, were undertaken in 32 men (mean age 53.6 +/- 8.1 years) with angiographically proven coronary heart disease. The protocol of the standardized exercise included bicycle ergometry, gymnastics, breathing and movement exercises, as well as nonstandardized walking or hiking. Following the training period the number of ischaemic episodes fell from 90 to 72 for the group as a whole and that of the asymptomatic episodes from 79 to 64. The number and severity of ventricular arrhythmias were similar during silent and symptomatic ischaemia. There was a significant increase in duration of exercise until reaching the ischaemia threshold (mean exercise duration 4.7 +/- 2.1 vs 5.9 +/- 2.5 min; P = 0.0007). There was no increased risk concerning ventricular arrhythmias.

Mitomycin C and mitoxantrone chemotherapy for advanced breast cancer: efficacy with minimal gastrointestinal toxicity and alopecia

In an attempt to examine the possibility of decreased toxicity in patients with advanced breast cancer who had not previously received chemotherapy, 33 women were given combination chemotherapy consisting of mitomycin C (10 mg/m2) every 6 weeks and mitoxantrone (6 mg/m2) every 3 weeks. The patients had predominantly visceral disease and received a median of two cycles of therapy. Of the 32 evaluable subjects, 15 (47%) achieved a partial response lasting a median of 7 months. Hematological toxicity was generally mild, although there were two episodes of sepsis. One patient developed hemolytic-uremic syndrome, and one subject developed pulmonary fibrosis, both presumably attributable to treatment with mitomycin C. Another patient died of hepatic failure (hepar lobatum). Thus, there were five patients who sustained life-threatening toxicities; this may have been due to the poor performance status and advanced age of some of the patients. Gastrointestinal toxicity and alopecia were minimal. Patient acceptance was high and there was an improvement in symptomatology in the majority of patients. In conclusion, mitomycin C and mitoxantrone chemotherapy is an active drug combination for the treatment of advanced breast cancer that seldom causes significant distressing gastrointestinal side effects or alopecia; however, the duration of response to this regimen appears to be shorter than that obtained with either cyclophosphamide - methotrexate - 5-fluorouracil (CMF) or cyclophosphamide - Adriamycin - 5-fluorouracil (CAF) combination chemotherapy.

Arrhythmogenic potential of exercise-induced myocardial ischaemia

In a series of 300 patients following transmural infarction undergoing coronary angiography because of ischaemia in the surviving myocardium, 17 demonstrated an exercise response indicative of myocardial ischaemia in the absence of angina pectoris. The presence of ischaemia in the region of the myocardium under scrutiny was proven by: (1) ST-segment depression during bicycle-ergometry of at least 2 mm in leads without any QRS or ST-T changes at rest. (2) greater than 75% stenosis of vessels supplying the area under investigation, in addition to the vessel supplying the region of the infarction. (3) A reversible Thallium-perfusion defect on exercise. We compared those 17 patients with silent myocardial ischaemia with 21 patients with typical angina pectoris on exertion. All patients underwent 24-hour Holter monitoring, treadmill exercise testing at a target heart rate previously determined as inducing signs of myocardial ischaemia, and swimming and calisthenic programs with telemetric ECG recording. There was no close relationship between myocardial ischaemia and the occurrence of complex ventricular arrhythmias. In silent ischaemia complex ventricular arrhythmias do not occur at a higher rate than in patients with angina pectoris.

[Carbohydrate-reduced diet and metabolism: about the influence of a 4-weeks isocaloric fat-rich, carbohydrate-reduced diet on body weight and metabolism (author's transl)]

The influence of a 4-weeks carbohydrate-reduced, fat-rich diet on 9 slightly overweighted men was investigated. Caloric and protein intake remained unchanged. 70% were fat, 20% carbohydrates. Glucose, cholesterol, triglycerids, serumproteins, urea, uric acid, sodium and potassium were measured in rest. At a 60 minutes bicycle ergometer test glucose, triglycerids, free fatty acids and glycerol were registrated before and after 20 and 60 minutes work. The intensity was 70% of the maximal oxygen uptake. These were the most important results: (1) Body weight decreased continuously. One of the causes is an elevated heat production. (2) Serum-protein and -glucose remained unchanged. Cholesterol and triglycerides were reduced. Serum-urea, -sodium and -potassium diminished continually. On the contrary, uric acid raised over the normal range. (3) Work performance was reduced for 20%. (4) Hypoglycemic values did not appear. The oxidation of fat by the working muscles, and fat mobilisation increased by a fat-rich diet.