Towards texture accurate slice interpolation of medical images using PixelMiner.

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review.

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

BACKGROUND: Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. OBJECTIVE: Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. STUDY TYPE: Prospective. POPULATION: 11 healthy female volunteers. FIELD STRENGTH/SEQUENCE: 1.5 T; MRI exams, comprising T2-weighted turbo spin-echo (T2W) sequence, native T1-weighted turbo gradient-echo (T1W) sequence, diffusion-weighted imaging (DWI) sequence using b-values 0/150/800, and corresponding derived ADC maps. ASSESSMENT: 18 MRI exams (three test-retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z-score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z-score normalization + grayscale discretization using 32 and 64 bins with and without BFC. STATISTICAL TESTS: Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut-off value of CCC > 0.90. RESULTS: Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z-score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. DATA CONCLUSION: Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability.

Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability.

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC).

OBJECTIVES: Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC. MATERIALS AND METHODS: Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients. RESULTS: Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like). CONCLUSION: LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.

Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review.

PURPOSE: MRI-based tumor response prediction to neoadjuvant systemic therapy (NST) in breast cancer patients is increasingly being studied using radiomics with outcomes that appear to be promising. The aim of this study is to systematically review the current literature and reflect on its quality. METHODS: PubMed and EMBASE databases were systematically searched for studies investigating MRI-based radiomics for tumor response prediction. Abstracts were screened by two reviewers independently. The quality of the radiomics workflow of eligible studies was assessed using the Radiomics Quality Score (RQS). An overview of the methodologies used in steps of the radiomics workflow and current results are presented. RESULTS: Sixteen studies were included with cohort sizes ranging from 35 to 414 patients. The RQS scores varied from 0 % to 41.2 %. Methodologies in the radiomics workflow varied greatly, especially region of interest segmentation, features selection, and model development with heterogeneous outcomes as a result. Seven studies applied univariate analysis and nine studies applied multivariate analysis. Most studies performed their analysis on the pretreatment dynamic contrast-enhanced T1-weighted sequence. Entropy was the best performing individual feature with AUC values ranging from 0.83 to 0.85. The best performing multivariate prediction model, based on logistic regression analysis, scored a validation AUC of 0.94. CONCLUSION: This systematic review revealed large methodological heterogeneity for each step of the MRI-based radiomics workflow, consequently, the (overall promising) results are difficult to compare. Consensus for standardization of MRI-based radiomics workflow for tumor response prediction to NST in breast cancer patients is needed to further improve research.

Selman A. Waksman, winner of the 1952 Nobel Prize for physiology or medicine.

The history of the discovery and development of streptomycin is reviewed here from the personal standpoint of a member of Dr. Selman Waksman's antibiotic screening research team. The team approach of eight individuals illustrates how the gradual enhancement of the screening methodology was developed. I illustrate three study periods with key aspects in the development of streptomycin which led to a Nobel Prize being granted to Professor Waksman. One item not previously emphasized is the employment of a submerged culture technique for large-scale production of streptomycin, thus enabling rapid animal testing and human clinical trials with Mycobacterium tuberculosis. Another is that purified streptomycin was shown by Dr. Waksman to be distinctly different from the substances called natural products, which are no longer patentable in the United States; therefore, streptomycin was found to be patentable. A third item not previously emphasized is his emphasis on the screening of actinomycetes, including the newly named Streptomyces genus. All of these factors contributed to the success of streptomycin in the treatment of tuberculosis. In combination, their successes led to Dr. Waksman's department becoming a new pharmacological research area, specializing in drug discovery. These unique accomplishments all burnish the prior rationales used by the Karolinska Institute in granting Dr. Waksman alone the 1952 Nobel Prize for Physiology or Medicine.

Ferroverdins, inhibitors of cholesteryl ester transfer protein produced by Streptomyces sp. WK-5344. I. Production, isolation and biological properties.

Streptomyces sp. WK-5344, a soil isolate, was found to produce structurally related inhibitors of cholesteryl ester transfer protein (CETP). New active compounds, designated ferroverdins B and C, were isolated along with known ferroverdin A from the fermentation broth by solvent extraction, ODS column chromatography and silica gel column chromatography. All ferroverdins showed a dose-dependent inhibitory activity against human CETP. The IC50 values were 21, 0.62 and 2.2 microM for ferroverdins A, B and C, respectively, indicating that ferroverdin B is one of the most potent CETP inhibitors of microbial origin.

Chloropeptins, new anti-HIV antibiotics inhibiting gp120-CD4 binding from Streptomyces sp. I. Taxonomy, fermentation, isolation, and physico-chemical properties and biological activities.

Chloropeptins I and II, which are gp120-CD4 binding inhibitors, were isolated as pale yellow-brown powders from the mycelia of a soil actinomycete, Streptomyces sp, WK-3419. Their physico-chemical properties showed that they are chlorinated peptides. Chloropeptin I (C61H45N7O15Cl6) is a novel compound, but chloropeptin II was identified as complestatin. Both compounds inhibited gp120-CD4 binding (IC50: 1.3 and 2.0 microM, respectively), the cytopathic effect of HIV in MT-4 cells (EC50: 1.6 and 1.7 microM, respectively) and syncytium formation in co-cultured HIV-1-infected and uninfected MOLT-4 cells (IC50. 0.5 and 1.1 microM, respectively). Chloropeptin I was synergistic in the inhibition of the cytopathic effect when combined with other anti-HIV drugs such as zidovudine (AZT), didanosine (ddI), zalcitabine (ddC) and nevirapine.

Louisianins A, B, C and D: non-steroidal growth inhibitors of testosterone-responsive SC 115 cells. II. Physico-chemical properties and structural elucidation.

New non-steroidal growth inhibitors of testosterone-responsive SC 115 cells, louisianins A (MW: 189; C11H11NO2), B (MW: 191; C11H13NO2), C (MW: 173; C11H11NO) and D (MW: 173; C11H11NO) were isolated from the cultured broth of Streptomyces sp. WK-4028. Their structures were determined on the basis of spectroscopic data. The structure of louisianin A in particular was confirmed by X-ray crystallographic analysis. The four compounds commonly possess a unique pyrindine skeleton in the molecule.

Louisianins A, B, C and D: non-steroidal growth inhibitors of testosterone-responsive SC 115 cells. I. Taxonomy, fermentation, isolation and biological characteristics.

In the course of screening for non-steroidal growth inhibitors of testosterone-responsive Shionogi carcinoma 115 cells, louisianins A, B, C and D, were isolated from fermentation broth of Streptomyces sp. WK-4028. Louisianin A remarkably inhibited the growth of SC 115 cells in the presence of 10(-7) M testosterone at an IC50 value of 0.6 microgram/ml, whereas, no inhibition was observed on the other cell lines. Furthermore, inhibitory activity of testosterone 5 alpha-reductase and antimicrobial activity have not been observed at a concentration of 50 micrograms/ml and 1,000 micrograms/ml, respectively.

Diolmycins, new anticoccidial agents produced by Streptomyces sp. I. Production, isolation and physico-chemical and biological properties.

Streptomyces sp. WK-2955, a soil isolate, was found to produce a series of new anticoccidial compounds. Four active compounds, designated diolmycins A1, A2, B1 and B2, were isolated from the fermentation broth of the producing strain by solvent extraction, silica gel column chromatography, gel filtration on Sephadex LH-20, and preparative HPLC. Diolmycins inhibited the growth of Eimeria tenella in an in vitro assay system using BHK-21 cells as a host. No schizont in the cells was observed at concentrations of 0.02-2.0 micrograms/ml for diolmycin A1, at 0.2-2.0 micrograms/ml for diolmycin A2, and at 20 micrograms/ml for diolmycins B1 and B2.

Studies on a novel antitumor antibiotic, phenazinomycin: taxonomy, fermentation, isolation, and physicochemical and biological characteristics.

A new antibiotic, phenazinomycin (C27H32N2O, MW 400), was isolated from the cultural mycelium of Streptomyces sp. WK-2057. This antibiotic possesses antibacterial activities against Gram-positive bacteria in vitro, direct cytotoxic activities against HeLa S3, P388 and P388 doxorubicin-resistant cells in vitro and antitumor activities against experimental murine tumors in vivo.

Ahpatinins, new acid protease inhibitors containing 4-amino-3-hydroxy-5-phenylpentanoic acid.

A soil isolate, Streptomyces sp. WK-142, was found to produce new acid protease inhibitors, ahpatinins A, B, D, E, F and G active against pepsin and renin. Ahpatinin C was found to be identical with pepstatin A. The structure determinations were based on mass spectral data. Four of the compounds contain the unusual amino acid, 4-amino-3-hydroxy-5-phenylpentanoic acid as a building component.

Use of a computer to group actinomycetes for studies on the ecology of soil microorganisms.

A computer program has been prepared for grouping soil actinomycetes into cluster groups based on the presence or absence of aerial mycelium, the color of soluble pigment, and the shade of color of surface and reverse mycelium. The program automatically condenses cultures with wide ranges of characteristics into a limited number of groups and provides a permanent record so that comparisons can be made among experiments performed over a span of time. The program permits the grouping of large numbers of cultures with minimal laboratory effort and has proven useful in defining some of the ecological factors that lead to changed actinomycete populations in soils.