[Fractal geometry in the objective grading of prostate carcinoma]

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network

BACKGROUND

The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.

METHODS

In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.

FINDINGS

The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.

INTERPRETATION

The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

Artificial Intelligence in Lung Cancer Pathology Image Analysis

OBJECTIVE

Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection.

MATERIALS AND METHODS

In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.

RESULTS

We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis.

DISCUSSION AND CONCLUSION

With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Two-dimensional neovascular complexity is significantly higher in nontumor prostate tissue than in low-risk prostate cancer

Purpose

Prostate cancer is the most frequent cancer in men in Europe. A major focus in urology is the identification of new biomarkers with improved accuracy in patients with low-risk prostate cancer. Here, we evaluated two-dimensional neovascular complexity in prostate tumor and nontumor biopsy cores by use of a computer-aided image analysis system and assessed the correlations between the results and selected clinical and pathological parameters of prostate carcinoma.

Materials and Methods

A total of 280 prostate biopsy sections from a homogeneous series of 70 patients with low-risk prostate cancer (Gleason score 3+3, prostate-specific antigen [PSA]<10 ng/mL, and clinical stage T1c) who underwent systematic biopsy sampling and subsequent radical prostatectomy were analyzed. For each biopsy, 2-µm sections were treated with CD34 antibodies and were digitized by using an image analysis system that automatically estimates the surface fractal dimension.

Results

Our results showed that biopsy sections without cancer were significantly more vascularized than were tumors. No correlations were found between the vascular surface fractal dimension and patient's age, PSA and free-to-total PSA ratios, pathological stage, Gleason score, tumor volume, vascular invasion, capsular penetration, surgical margins, and biochemical recurrence.

Conclusions

The value of angiogenesis in prostate cancer is still controversial. Our findings suggest that low-risk prostate cancer tissues are less vascularized than are nontumor tissues. Further studies are necessary to understand whether angiogenesis is a hallmark of intermediate- and high-risk prostate cancer.