Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study

Objectives Multiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade. Methods We retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements. Results Fractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2–5) cancer with a sensitivity of 91% (confidence interval [CI]: 83–96%) and a specificity of 86% (CI: 73–94%). FD correlated linearly with ISUP groups (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1–4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001). Conclusion Fractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors. Key Points • In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1–4). • Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83–96%) and a specificity of 86% (73–94%). • Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.

Raman Spectroscopy and Oncology: Multivariate Statistics Methods for Cancer Grading

Over the last decade, Raman spectroscopy was demonstrated as a label-free and destructive optical spectroscopy that was able to improve diagnostic accuracy in cancer diagnosis. This ability is principally based on the great amount of biochemical information produced by the Raman scattering while investigating biological tissues. However, to achieve the relevant clinical requirements, the spectroscopic analysis and its ability to grade cancer tissues require sophisticated multivariate statistics. In this paper, we critically review multivariate statistics methods analyzed in light of their ability to process datasets generated by Raman spectroscopy in chondrogenic tumors, where distinguishing between enchondroma and the first grade of malignancy is a critical problem for pathologists.

Significant Inter- and Intralaboratory Variation in Gleason Grading of Prostate Cancer: A Nationwide Study of 35,258 Patients in The Netherlands

Purpose: Our aim was to analyze grading variation between pathology laboratories and between pathologists within individual laboratories using nationwide real-life data. Methods: We retrieved synoptic (n = 13,397) and narrative (n = 29,377) needle biopsy reports from the Dutch Pathology Registry and prostate-specific antigen values from The Netherlands Cancer Registration for prostate cancer patients diagnosed between January 2017 and December 2019. We determined laboratory-specific proportions per histologic grade and unadjusted odds ratios (ORs) for International Society of Urological Pathologists Grades 1 vs. 2–5 for 40 laboratories due to treatment implications for higher grades. Pathologist-specific proportions were determined for 21 laboratories that consented to this part of analysis. The synoptic reports of 21 laboratories were used for analysis of case-mix correction for PSA, age, year of diagnosis, number of biopsies and positive cores. Results: A total of 38,321 reports of 35,258 patients were included. Grade 1 ranged between 19.7% and 44.3% per laboratory (national mean = 34.1%). Out of 40 laboratories, 22 (55%) reported a significantly deviant OR, ranging from 0.48 (95% confidence interval (CI) 0.39–0.59) to 1.54 (CI 1.22–1.93). Case-mix correction was performed for 10,294 reports, altering the status of 3/21 (14%) laboratories, but increasing the observed variation (20.8% vs. 17.7%). Within 15/21 (71%) of laboratories, significant inter-pathologist variation existed. Conclusion: Substantial variation in prostate cancer grading was observed between and within Dutch pathology laboratories. Case-mix correction did not explain the variation. Better standardization of prostate cancer grading is warranted to optimize and harmonize treatment.

Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.

Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading

Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to segment tumour regions. In this study, a simple approach of segmentation was proposed to segment tumour region on breast histopathology images. The proposed segmentation involved three stages: pre-processing, segmentation and post-processing. The proposed approach using GHE and median filter in the pre-processing stage; Otsu thresholding in the segmentation stage and; morphological operation and pixel removal in the post-processing stage was found able to segment the tumour region with average segmentation accuracy of 90.4 %.

Correlation between Age and Body Mass Index (BMI) with Histopathological Features of Breast Cancer Patients in RSUP Haji Adam Malik Medan

Background: Breast cancer is influenced by various risk factors, including age and obesity. Older women who are overweighted and obese have a higher risk of developing breast cancer. This study aims to find the correlation between age and body mass index (BMI) with histopathological features of breast cancer patients in RSUP Haji Adam Malik Medan.Methods: This research is an analytical study using a cross-sectional design with 103 samples obtained from medical record data by random sampling. The data obtained were then adjusted to the research criteria and grouped based on predetermined variables.Results: The majority of the age group was 41-50 years (36.9%), obesity BMI (40.8%), histopathological subtype of invasive carcinoma of no special type (NST) (85.4%), and grade II (46.6%). Kruskal-Wallis test result between histopathological subtypes and breast cancer grading based on age, respectively, obtained p=0.503, r=.325; (α>0.05), and p=0.207, r=0.393; (α>0.05), while based on BMI obtained p=0.017, r=0.021; (α<0.05), and p=0.018, r=0.018; (α<0.05). The OR value (95% CI) obtained on overweight-obese BMI with invasive carcinoma NST subtype was 7.63 (7.27–14.90) and other subtypes were 2.40 (1.14–13.75), and for grades II and III, they were respectively 3.57 (1.32–8.09) and 3.27 (1.17–9.91).Conclusions: There was a correlation between BMI with histopathological subtypes and breast cancer grading, but the correlation tended to be weak. Whereas, with age, there was no correlation. BMI overweight-obese were more likely to have invasive carcinoma NST subtype and higher-grade of breast cancer.

Grading of invasive breast carcinoma: the way forward

Histologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histologic grading needs to be performed accurately, on properly fixed specimens, and by adequately trained dedicated pathologists that take the time to diligently follow the protocol methodology. In this paper, we review the history of histologic grading, describe the basics of grading, review prognostic value and reproducibility issues, compare performance of grading to gene expression profiles, and discuss how to move forward to improve reproducibility of grading by training, feedback and artificial intelligence algorithms, and special stains to better recognize mitoses. We conclude that histologic grading, when adequately carried out, remains to be of important prognostic value in breast cancer patients.