Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.

In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases limits the prediction accuracy of a single model. In this study, we propose a novel pipeline system that uses three independent models each focusing on different characteristics of cancer atypia. Our system uses a CNN model to learn structural atypia from image patches and SVM and random forest models to learn nuclear atypia from fine-grained nuclear features extracted by image analysis methods. It was able to predict the NAC response with 95.15% accuracy on a test set of 103 unseen cases. We believe that this AI pipeline system will contribute to the adoption of personalized medicine in NAC therapy for breast cancer.

Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used; data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning.

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

Pathological Diagnosis of Gastric Cancers with a Novel Computerized Analysis System.

BACKGROUND: Recent studies of molecular biology have provided great advances for diagnostic molecular pathology. Automated diagnostic systems with computerized scanning for sampled cells in fluids or smears are now widely utilized. Automated analysis of tissue sections is, however, very difficult because they exhibit a complex mixture of overlapping malignant tumor cells, benign host-derived cells, and extracellular materials. Thus, traditional histological diagnosis is still the most powerful method for diagnosis of diseases. METHODS: We have developed a novel computer-assisted pathology system for rapid, automated histological analysis of hematoxylin and eosin (H and E)-stained sections. It is a multistage recognition system patterned after methods that human pathologists use for diagnosis but harnessing machine learning and image analysis. The system first analyzes an entire H and E-stained section (tissue) at low resolution to search suspicious areas for cancer and then the selected areas are analyzed at high resolution to confirm the initial suspicion. RESULTS: After training the pathology system with gastric tissues samples, we examined its performance using other 1905 gastric tissues. The system's accuracy in detecting malignancies was shown to be almost equal to that of conventional diagnosis by expert pathologists. CONCLUSIONS: Our novel computerized analysis system provides a support for histological diagnosis, which is useful for screening and quality control. We consider that it could be extended to be applicable to many other carcinomas after learning normal and malignant forms of various tissues. Furthermore, we expect it to contribute to the development of more objective grading systems, immunohistochemical staining systems, and fluorescent-stained image analysis systems.

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

BACKGROUND: Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. METHODS AND RESULTS: In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CONCLUSION: CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

Are the votes of ethics committees in Germany for the protection of clinical study trial subjects "sovereign acts?".

A sudden paradigm shift has resulted in governmental measures that greatly impact the scope in which the ethics committees in Germany can perform their task of providing expert opinions for clinical research. The so-called "revaluation" of the Medical Device Law Deutsches Medizinproduktegesetz-MPG) is, in our opinion, not based on sound political and professional judgment. In accordance with the changed regulations, ethics committees are now seen as being sub-organs of the state medical associations or the medical faculties and are therefore official authorities. It follows that the votes of ethics committees are then "sovereign acts" or authoritative measures! However, equality and justice speak against this misleading conclusion and its resulting consequence that an ethics committee's vote is a sovereign act. This has, in turn, resulted in the public ethics committees obtaining their long-sought goal of having a state-sanctioned monopoly. The private ethics committees are not recognized as being authoritative bodies, nor are they to be seen as such in the future (i.e. such a status has been denied the Freiburg Ethics Commission International (FEKI) in Baden-Württemberg). This political mistake must be corrected, otherwise, conducting clinical research will become increasingly difficult.

Mitotic figure recognition: agreement among pathologists and computerized detector.

Despite the prognostic importance of mitotic count as one of the components of the Bloom-Richardson grade, several studies have found that pathologists' agreement on the mitotic grade is fairly modest. Collecting a set of more than 4,200 candidate mitotic figures, we evaluate pathologists' agreement on individual figures, and train a computerized system for mitosis detection, comparing its performance to the classifications of three pathologists. The system's and the pathologists' classifications are based on evaluation of digital micrographs of hematoxylin and eosin stained breast tissue. On figures where the majority of pathologists agree on a classification, we compare the performance of the trained system to that of the individual pathologists. We find that the level of agreement of the pathologists ranges from slight to moderate, with strong biases, and that the system performs competitively in rating the ground truth set. This study is a step towards automatic mitosis count to accelerate a pathologist's work and improve reproducibility.

Mitotic index of invasive breast carcinoma. Achieving clinically meaningful precision and evaluating tertial cutoffs.

CONTEXT: Mitotic figure counts are related to breast cancer behavior but have not been sufficiently reproducible to be accepted for clinical decision-making. OBJECTIVE: To improve reproducibility and accuracy of the mitotic count. DESIGN: Mitotic index (MI) was defined as the mitotic cell count per 10 high-power fields (HPFs), an area 0.183 mm(2). Two to 6 replicate sets of 10 HPFs were counted from 328 invasive breast carcinomas. Standard errors and coefficients of variation for mean MI were compared with expected results predicted by the binomial distribution. RESULTS: The boundaries for MI that separated the data into equal thirds (tertials) were 1.14 and 5.33. Standard errors and coefficients of variation for MI followed distributions predicted by binomial probability. Mean coefficient of variation was 147% for the low tertial, 72% for the midtertial, and 34.6% for the upper tertial. CONCLUSIONS: Standard errors for MI based on a single count of 10 HPFs are too broad and coefficients of variation too large to be acceptable for clinical use. This is explained as a binomial probability effect, possibly with a contribution from tumor heterogeneity. Errors can be reduced in proportion to the square root of the number of sets of 10 HPFs counted. Tertial cutoffs of MI of the Nottingham system currently used in breast carcinoma grading are too high to be applicable to the population we studied. We recommend validation of cutoffs before they are applied to a particular population of breast carcinomas. Counting 5 sets of 10 HPFs is necessary to accurately rank carcinomas with low MIs.

Swiss psychiatrists beliefs and attitudes about cannabis risks in psychiatric patients: ideologically determined or evidence-based?

The objective of this survey was to assess the beliefs of Swiss psychiatrists about the risks associated with cannabis, and to assess their prohibitive attitudes toward their patients. Eighty-two doctors agreed to fill-up the questionnaire. Cluster analysis retained a 3-cluster solution. Cluster 1: "Prohibitionists" believed that cannabis could induce and trigger all forms of psychiatric disorder, and showed a highly prohibitive attitude. Cluster 2: "Causalists" believed that schizophrenia, but not other psychiatric disorders, could be induced and triggered. Cluster 3: "Prudent liberals" did not believe that psychiatric disorders could be induced by cannabis, and were generally less prohibitive.