Clinical Lab Microscopy & Imaging: Literature Review

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia 

Soya E, Ekenel N, Savas R et al. J Clin Imaging Sci 2022;12:6 doi: 10.25259/JCIS_172_2021 

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia burden of COVID-19 disease. However, due to the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous artificial intelligence (AI) software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes.

Material and methods: Fifty COVID-19 and 50 age-matched negative control patients were analysed using the proposed technique and commercially available AI software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts.

Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between –642.4 Hounsfield unit (HU) and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1±4.4%, 6.1±4.5, and 11.3±7.3% for the COVID-19 cohort, respectively, and statistically different (P<0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P<0.01).

Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

Gene of the month: NKX3.1

Griffin J, Chen Y, Catto JWF, El-Khamisy S. J Clin Pathol 2022;75(6):361–364

NKX3.1 is a multifaceted protein with roles in prostate development and protection from oxidative stress. Acting as a pioneer factor, NKX3.1 interacts with chromatin at enhancers to help integrate androgen regulated signalling. In prostate cancer, NKX3.1 activity is frequently reduced through a combination of mutational and post-translational events. Owing to its specificity for prostate tissue, NKX3.1 has found use as an immunohistochemical marker in routine histopathology practice.

Sharp-GAN: sharpness loss regularized GAN for histopathology image synthesis

Butte S, Wang H, Xian M, Vakanski A. Proc IEEE Int Symp Biomed Imaging 2022;2022:10.1109/isbi52829.2022.9761534

Existing deep learning-based approaches for histopathology image analysis require large annotated training sets to achieve good performance; but annotating histopathology images is slow and resource-intensive. Conditional generative adversarial networks have been applied to generate synthetic histopathology images to alleviate this issue, but current approaches fail to generate clear contours for overlapped and touching nuclei. In this study, We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images. The proposed network uses normalized nucleus distance map rather than the binary mask to encode nuclei contour information. The proposed sharpness loss enhances the contrast of nuclei contour pixels. The proposed method is evaluated using four image quality metrics and segmentation results on two public datasets. Both quantitative and qualitative results demonstrate that the proposed approach can generate realistic histopathology images with clear nuclei contours.

A myriad spectrum of seizures on magnetic resonance imaging – a pictorial essay

Lingutla RK, Mahale A, Bhat AR, Ullal S. J Clin Imaging Sci 2022;12:3 doi: 10.25259/JCIS_124_2020

Patients with seizures represent a challenging clinical population both in pediatrics and adults. Accurate diagnosis of the cause of a seizure is important in choosing an effective treatment modality, surgical planning, predicting a prognosis, and follow-up. Magnetic resonance (MR) imaging using a dedicated epilepsy protocol plays a key role in the workup of these patients. Additional MR techniques such as T2 relaxometry and MR spectroscopy show a promising role to arrive at a final diagnosis. The spectrum of epileptogenic causes is broad. Radiologists and physicians need to be updated and require a patterned approach in light of clinical history and electroencephalogram findings to arrive at a reasonable differential diagnosis. This pictorial essay aims to review a few of the common and uncommon causes of seizures and their imaging features.

Validating whole slide imaging systems for diagnostic purposes in pathology

Evans AJ, Brown RW, Bui MM et al. Arch Pathol Lab Med 2022;146(4):440–450

Context: The original guideline, “Validating Whole Slide Imaging for Diagnostic Purposes in Pathology,” was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines.

Objective: To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes.

Design: An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations.

Results: Three strong recom-mendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems.

Conclusions: Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%.

Developing digital photomicroscopy

Micklem K. Cells 2022;11(2):296

(1) The need for efficient ways of recording and presenting multicolour immunohistochemistry images in a pioneering laboratory developing new techniques motivated a move away from photography to electronic and ultimately digital photomicroscopy. (2) Initially broadcast quality analogue cameras were used in the absence of practical digital cameras. This allowed the development of digital image processing, storage and presentation. (3) As early adopters of digital cameras, their advantages and limitations were recognized in implementation. (4) The adoption of immunofluorescence for multiprobe detection prompted further developments, particularly a critical approach to probe colocalization. (5) Subsequently, whole-slide scanning was implemented, greatly enhancing histology for diagnosis, research and teaching.

Whole slide imaging vs eyeballing: the future in quantification of tubular atrophy in routine clinical practice

Gupta K, Maitra D, Gowrishankar S. Indian J Nephrol 2022;32(2):151–155

Introduction: Histologic assessment of interstitial fibrosis and tubular atrophy is an accepted method of assessing chronic damage to the kidney and correlates with renal function in native and allograft renal biopsies. The challenge, however, is to quantify the interstitial fibrosis and tubular atrophy with accuracy and to minimize the inter-observer variability. Though ‘eyeballing’ on light microscopy is the most commonly practised method used for the quantification of tubular atrophy, it may not be very accurate. To complement this method, whole slide imaging (WSI) techniques that have more accurate results and have a higher reproducibility can be used. There is not much data on the correlation of the results obtained by the ‘eyeballing’ technique with those by digital WSI.

Methods: Tubular atrophy in 151 consecutive adequate native kidney biopsies were graded 0 to III by ‘conventional’ eyeballing by a single experienced renal pathologist. These results were compared with the grades obtained on the same cases by WSI and digital marking of the atrophy.

Results: The concordance of the two groups in the entire cohort was only 66.2% with over grading in 30.4% and under grading in 3.3%. Whilst accuracy of grading was over 74% in all grades, the sensitivity in grades I and II were low at 52% and 47.3% respectively as was the positive predictive value at 32.5 and 44% respectively.

Conclusion: Assessment of tubular atrophy on digital images will be the way forward for accurate quantification.

Imaging surveillance of the reconstructed breast in a subset of patients may aid in early detection of breast cancer recurrence

Adrada BE, Karbasian N, Huang M et al. J Clin Imaging Sci 2021;11:58 doi: 10.25259/JCIS_113_2021

Objectives: The purpose of this study is to determine the biological markers more frequently associated with recurrence in the reconstructed breast, to evaluate the detection method, and to correlate recurrent breast cancers with the detection method.

Material and methods: An institutional review board-approved retrospective study was conducted at a single institution on 131 patients treated with mastectomy for primary breast cancer followed by breast reconstruction between 2005 and 2012. Imaging features were correlated with clinical and pathologic findings.

Results: Of the 131 patients who met our inclusion criteria, 40 patients presented with breast cancer recurrence. The most common histopathologic type of primary breast cancer was invasive ductal carcinoma in 82.5% (33/40) of patients. Triple-negative breast cancer was the most common biological marker with 42.1% (16/38) of cases. Clinically, 70% (28/40) of the recurrences presented as palpable abnormalities. Of nine patients who underwent mammography, a mass was seen in eight patients. Of the 35 patients who underwent ultrasound evaluation, an irregular mass was found in 48.6% (17/35) of patients. Nine patients with recurrent breast cancer underwent breast MRI, and MRI showed an irregular enhancing mass in four patients, an oval mass in four patients, and skin and trabecular thickening in one patient. About 55% of patients with recurrent breast cancer were found to have distant metastases.

Conclusion: Patients at higher risk for locoregional recurrence may benefit from imaging surveillance in order to detect early local recurrences.

Intraoperative mass spectrometry platform for IDH mutation status prediction, glioma diagnosis, and estimation of tumor cell infiltration

Brown HM, Alfaro CM, Pirro V et al. J Appl Lab Med 2021;6(4):902–916

Background: Surgical tumour resection is the primary treatment option for diffuse glioma, the most common malignant brain cancer. The intraoperative diagnosis of gliomas from tumour core samples can be improved by use of molecular diagnostics. Further, residual tumour at surgical margins is a primary cause of tumour recurrence and malignant progression. This study evaluates a desorption electrospray ionization mass spectrometry (DESI-MS) system for intraoperative isocitrate dehydrogenase (IDH) mutation assessment, estimation of tumour cell infiltration as tumour cell percentage (TCP), and disease status. This information could be used to enhance the extent of safe resection and so potentially improve patient outcomes.

Methods: A mobile DESI-MS instrument was modified and used in neurosurgical operating rooms (ORs) on a cohort of 49 human subjects undergoing craniotomy with tumour resection for suspected diffuse glioma. Small tissue biopsies (ntotal=203) from the tumour core and surgical margins were analysed by DESI-MS in the OR and classified using univariate and multivariate statistical methods.

Results: Assessment of IDH mutation status using DESI-MS/MS to measure 2-hydroxyglutarate (2-HG) ion intensities from tumour cores yielded a sensitivity, specificity, and overall diagnostic accuracy of 89, 100, and 94%, respectively (ncore=71). Assessment of TCP (categorized as low or high) in tumour margin and core biopsies using N-acetyl-aspartic acid (NAA) intensity provided a sensitivity, specificity, and accuracy of 91, 76, and 83%, respectively (ntotal=203). TCP assessment using lipid profile deconvolution provided sensitivity, specificity, and accuracy of 76, 85, and 81%, respectively (ntotal=203). Combining the experimental data and using PCA-LDA predictions of disease status, the sensitivity, specificity, and accuracy in predicting disease status are 63%, 83%, and 74%, respectively (ntotal=203).

Conclusions: The DESI-MS system allowed for identification of IDH mutation status, glioma diagnosis, and estimation of tumour cell infiltration intraoperatively in a large human glioma cohort. This methodology should be further refined for clinical diagnostic applications.