Artificial Intelligence Role in Subclassifying Cytology of Thyroid Follicular Neoplasm

OBJECTIVE

Fine needle aspiration cytology has higher sensitivity and predictive value for diagnosis of thyroid nodules than any other single diagnostic methods.  In the Bethesda system for reporting thyroid, the category IV, encompasses both adenoma and carcinoma, but it is not possible to differentiate both lesions in the cytology practice and can be only differentiated after resection. In this work, we aim at exploring the ability of a convolutional neural network (CNN) model to sub-classifying cytological images of Bethesda category IV diagnosis into follicular adenoma and follicular carcinoma.

METHODS

We used a cohort of cytology cases n= 43 with extracted images n= 886 to train CNN model aiming to sub-classify follicular neoplasm (Bethesda category IV) into either follicular adenoma or follicular carcinoma.

RESULT

In our study, the model subclassification of follicular neoplasm into follicular adenoma (n = 28/43, images n = 527/886) from follicular carcinoma (n = 15/43, images n= 359/886), has achieved an accuracy of 78%, with a sensitivity of 88.4%, and a specificity of 64% and an area under the curve (AUC) score of 0.87 for each of follicular adenoma and follicular carcinoma.

CONCLUSION

Our CNN model has achieved high sensitivity in recognizing follicular adenoma amongest cytology smears of follciualr neoplasms, thus it can be used as an ancillary technique in the subcalssification of Bethesda Iv category cytology smears.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

Background

Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.

Results

This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes.

Conclusions

This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings

MOTIVATION

Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative and/or nonintuitive to pathologists.

RESULTS

In this article, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step toward realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers.

AVAILABILITY AND IMPLEMENTATION

Relevant code can be found at github.com/CancerDataScience/NuCLS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Operative management and outcome in children with pheochromocytoma

OBJECTIVE

This study aimed to evaluate management and prognosis in children with pheochromocytoma who were treated at an Egyptian tertiary center.

METHODS

The authors conducted an 8-year retrospective analysis for 17 patients who were presented from January 2013 to January 2021. Clinical criteria, operative details, and follow-up data were assessed. Overall (OS) and event-free survival (EFS) were estimated by the Kaplan-Meier method. An event was assigned with the occurrence of recurrence or metachronous disease, or death.

RESULTS

Median age at diagnosis was 14 years (range: 6-17.5 years). Ten patients (58.8%) were males and seven (41.2%) were females. Hypertension-related symptoms were the main presentations in 15 patients (88%). None of the included children underwent genetic testing. Sixteen patients (94%) had unilateral tumors (right side: 12), whereas only one was presented with bilateral masses. The median tumor size was 7 cm (range: 4-9 cm). Metastatic workup did not reveal any metastatic lesions. All patients underwent open adrenalectomy, and clinical manifestations were completely resolved after surgery. Adjuvant therapy was not administered to any patient. There were no deaths or relapses at a median follow-up time of 40 months, whilst two children had metachronous disease after primary resection. Both were managed by adrenal-sparing surgery, and they achieved a second complete remission thereafter. Five-year OS and EFS were 100% and 88%, respectively.

CONCLUSIONS

Complete surgical resection achieves excellent clinical and survival outcomes for pheochromocytoma in children. Meticulous, long-term follow-up is imperative for early detection of metachronous disease to facilitate adrenal-sparing surgery. Genetic assessment for patients and their families is essential; however, it was not available at our institution.

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. SIGNIFICANCE: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.

Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tumor Tissue to Identify Single-Cell-Level PI3K Pathway Activation

PURPOSE

Identifying cancers with high PI3K pathway activity is critical for treatment selection and eligibility into clinical trials of PI3K inhibitors. Assessments of tumor signaling pathway activity need to consider intratumoral heterogeneity and multiple regulatory nodes.

EXPERIMENTAL DESIGN

We established a novel, mechanistically informed approach to assessing tumor signaling pathways by quantifying single-cell-level multiplex immunofluorescence using custom algorithms. In a proof-of-concept study, we stained archival formalin-fixed, paraffin-embedded (FFPE) tissue from patients with primary prostate cancer in two prospective cohort studies, the Health Professionals Follow-up Study and the Physicians' Health Study. PTEN, stathmin, and phospho-S6 were quantified on 14 tissue microarrays as indicators of PI3K activation to derive cell-level PI3K scores.

RESULTS

In 1,001 men, 988,254 tumor cells were assessed (median, 743 per tumor; interquartile range, 290-1,377). PI3K scores were higher in tumors with PTEN loss scored by a pathologist, higher Gleason grade, and a new, validated bulk PI3K transcriptional signature. Unsupervised machine-learning approaches resulted in similar clustering. Within-tumor heterogeneity in cell-level PI3K scores was high. During long-term follow-up (median, 15.3 years), rates of progression to metastases and death from prostate cancer were twice as high in the highest quartile of PI3K activation compared with the lowest quartile (hazard ratio, 2.04; 95% confidence interval, 1.13-3.68).

CONCLUSIONS

Our novel pathway-focused approach to quantifying single-cell-level immunofluorescence in FFPE tissue identifies prostate tumors with PI3K pathway activation that are more aggressive and may respond to pathway inhibitors.

High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program

Systemic metabolic alterations associated with increased consumption of saturated fat and obesity are linked with increased risk of prostate cancer progression and mortality, but the molecular underpinnings of this association are poorly understood. Here, we demonstrate in a murine prostate cancer model, that high-fat diet (HFD) enhances the MYC transcriptional program through metabolic alterations that favour histone H4K20 hypomethylation at the promoter regions of MYC regulated genes, leading to increased cellular proliferation and tumour burden. Saturated fat intake (SFI) is also associated with an enhanced MYC transcriptional signature in prostate cancer patients. The SFI-induced MYC signature independently predicts prostate cancer progression and death. Finally, switching from a high-fat to a low-fat diet, attenuates the MYC transcriptional program in mice. Our findings suggest that in primary prostate cancer, dietary SFI contributes to tumour progression by mimicking MYC over expression, setting the stage for therapeutic approaches involving changes to the diet.

Structured crowdsourcing enables convolutional segmentation of histology images

MOTIVATION

While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.

RESULTS

We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy.

AVAILABILITY AND IMPLEMENTATION

Dataset is freely available at: https://goo.gl/cNM4EL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract 2436: Systematic computational analysis of histologic-genomic associations in triple-negative infiltrating ductal carcinomas of the breast

Background: Triple-negative breast cancer (TNBC) is characterized by rapid progression and lack of therapeutic targets. There is a pressing need for in-depth characterization of the biological correlates (and potential prognostic biomarkers) in TNBC. We performed a systematic analysis of genomic correlates of histologic markers of tumor aggressiveness and immune infiltration in 125 infiltrating ductal TNBC patients from The Cancer Genome Atlas.

Methods: Fully-convolutional networks of the VGG16-FCN8 architecture were trained to classify various tissue regions in H&E slides (0.956 AUC on unseen slides), and morphologic descriptors of tumor aggressiveness, invasion, and immune infiltration were extracted from predictions. Expression levels of 17,052 genes were entered into sample purity-adjusted linear regression models, and a Gene Set Enrichment Analysis was performed using the model coefficients to find gene set-level associations.

Results: Significant associations are summarized in Table 1. Expression of mTOR pathway genes (especially HIGD1C and PDE6H) is positively associated with large, dense tumor nests with a smooth tumor-stroma boundary. Boundary complexity is also positively associated with the oxidant stress response of NFE2L2. In contrast, some genes downregulated by the TGF-β pathway (including FGF6, PSG2 and CHRNG) are associated with a large tumor nest phenotype. The cell cycle regulator E2F1 (through PRM1, KRT72, and DBF4) is associated with dense immune infiltration, a known marker of good prognosis, and also small tumor nest size.

Conclusion: mTOR and NFE2L2-mediated mechanisms are significantly associated with features of tumor aggressiveness in TNBC, while E2F and some TGF-β targets are associated with morphological markers of less aggressive tumors. Further research is needed to elucidate the biological basis of these associations and their potential significance in therapeutic targeting of TNBC.

Table 1:Significant histologic-genomic associations in infiltrating-ductal TNBC.Histologic phenotype (feature description)Enriched gene set (MSigDB Oncogenic Signatures)Gene set descriptionNES(P-value, FDR)Leading-edge genesTumor nest size (Mean tumor nest area)TGFB_UP.V1_DNGenes down-regulated by TGFB11.55(p<0.001, FDR=0.036)FGF6; PSG2; CHRNGTumor-stroma interface (non-)complexity (mean solidity of tumor nests)NFE2L2.V2Genes upregulated with knockout of NFE2L2 gene (involved in oxidant stress response and inflammation)-1.51(p<0.001, FDR=0.027)DEFB119; CNTNAP5; SCGB1D1MTOR_UP.V1_DNGenes downregulated by everolimus (an mTOR inhibitor)1.43(p=0.0017, FDR= 0.094)HIGD1CSmall, solitary tumor nestsE2F1_UP.V1_UPGenes up-regulated when E2F1 is over-expressed (cell cycle regulation)1.48(p<0.001, FDR<0.001)PRM1MTOR_UP.V1_DNGenes downregulated by everolimus (an mTOR inhibitor)-1.50(p=0.0027, FDR= 0.079)PDE6H; HIGD1CSmall tumor nests with abundant surrounding immune infiltration (a spatial descriptor meant to capture immune success)E2F1_UP.V1_UPGenes up-regulated when E2F1 is over-expressed.(cell cycle regulation)1.55(p<0.001, FDR= 0.0019)PRM1; KRT72; DBF4

Citation Format: Mohamed Amgad, Uday Kurkure, Habiba Elfandy, Hagar H. Khallaf, David A. Gutman, Carlos S. Moreno, Michael Barnes, Lee A. Cooper. Systematic computational analysis of histologic-genomic associations in triple-negative infiltrating ductal carcinomas of the breast [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2436.

Genetic and Epigenetic Determinants of Aggressiveness in Cribriform Carcinoma of the Prostate

Among prostate cancers containing Gleason pattern 4, cribriform morphology is associated with unfavorable clinicopathologic factors, but its genetic features and association with long-term outcomes are incompletely understood. In this study, genetic, transcriptional, and epigenetic features of invasive cribriform carcinoma (ICC) tumors were compared with non-cribriform Gleason 4 (NC4) in The Cancer Genome Atlas (TCGA) cohort. ICC (n = 164) had distinctive molecular features when compared with NC4 (n = 102). These include: (i) increased somatic copy number variations (SCNV), specifically deletions at 6q, 8p and 10q, which encompassed PTEN and MAP3K7 losses and gains at 3q; (ii) increased SPOP ^mut and ATM^mut ; (iii) enrichment for mTORC1 and MYC pathways by gene expression; and (iv) increased methylation of selected genes. In addition, when compared with the metastatic prostate cancer, ICC clustered more closely to metastatic prostate cancer than NC4. Validation in clinical cohorts and genomically annotated murine models confirmed the association with SPOP^mut (n = 38) and PTEN^loss (n = 818). The association of ICC with lethal disease was evaluated in the Health Professionals Follow-up Study (HPFS) and Physicians' Health Study (PHS) prospective prostate cancer cohorts (median follow-up, 13.4 years; n = 818). Patients with ICC were more likely to develop lethal cancer [HR, 1.62; 95% confidence interval (CI), 1.05-2.49], independent from Gleason score (GS). IMPLICATIONS: ICC has a distinct molecular phenotype that resembles metastatic prostate cancer and is associated with progression to lethal disease.