Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk

Bridging Histopathology and Radiomics Toward Prognosis of Metastasis in Early Breast Cancer

Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.

RNA methylation-related inhibitors: Biological basis and therapeutic potential for cancer therapy

RNA methylation is widespread in nature. Abnormal expression of proteins associated with RNA methylation is strongly associated with a number of human diseases including cancer. Increasing evidence suggests that targeting RNA methylation holds promise for cancer treatment. This review specifically describes several common RNA modifications, such as the relatively well-studied N6-methyladenosine, as well as 5-methylcytosine and pseudouridine (Ψ). The regulatory factors involved in these modifications and their roles in RNA are also comprehensively discussed. We summarise the diverse regulatory functions of these modifications across different types of RNAs. Furthermore, we elucidate the structural characteristics of these modifications along with the development of specific inhibitors targeting them. Additionally, recent advancements in small molecule inhibitors targeting RNA modifications are presented to underscore their immense potential and clinical significance in enhancing therapeutic efficacy against cancer. KEY POINTS: In this paper, several important types of RNA modifications and their related regulatory factors are systematically summarised. Several regulatory factors related to RNA modification types were associated with cancer progression, and their relationships with cancer cell migration, invasion, drug resistance and immune environment were summarised. In this paper, the inhibitors targeting different regulators that have been proposed in recent studies are summarised in detail, which is of great significance for the development of RNA modification regulators and cancer treatment in the future.

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

OBJECTIVES

This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.

METHODS

CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis.

RESULTS

The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC).

CONCLUSION

Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods

AIMS

Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software.

METHODS AND RESULTS

TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores.

CONCLUSIONS

We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.

Taking tumour budding to the next frontier - a post International Tumour Budding Consensus Conference (ITBCC) 2016 review

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence-based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.