1
|
Salvi M, Manini C, López JI, Fenoglio D, Molinari F. Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase. Comput Med Imaging Graph 2023; 109:102288. [PMID: 37633031 DOI: 10.1016/j.compmedimag.2023.102288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/12/2023] [Accepted: 08/12/2023] [Indexed: 08/28/2023]
Abstract
BACKGROUND Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining. METHODS This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update. RESULTS Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability. CONCLUSIONS Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer.
Collapse
Affiliation(s)
- Massimo Salvi
- Biolab, PoliToBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
| | - Claudia Manini
- Department of Pathology, San Giovanni Bosco Hospital, 10154 Turin, Italy; Department of Sciences of Public Health and Pediatrics, University of Turin, 10124 Turin, Italy
| | - Jose I López
- Biomarkers in Cancer Group, Biocruces-Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Dario Fenoglio
- Biolab, PoliToBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Filippo Molinari
- Biolab, PoliToBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| |
Collapse
|
2
|
Bashkanov O, Rak M, Meyer A, Engelage L, Lumiani A, Muschter R, Hansen C. Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 239:107624. [PMID: 37271051 DOI: 10.1016/j.cmpb.2023.107624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 05/13/2023] [Accepted: 05/25/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE With emerging evidence to improve prostate cancer (PCa) screening, multiparametric magnetic prostate imaging is becoming an essential noninvasive component of the diagnostic routine. Computer-aided diagnostic (CAD) tools powered by deep learning can help radiologists interpret multiple volumetric images. In this work, our objective was to examine promising methods recently proposed in the multigrade prostate cancer detection task and to suggest practical considerations regarding model training in this context. METHODS We collected 1647 fine-grained biopsy-confirmed findings, including Gleason scores and prostatitis, to form a training dataset. In our experimental framework for lesion detection, all models utilized 3D nnU-Net architecture that accounts for anisotropy in the MRI data. First, we explore an optimal range of b-values for diffusion-weighted imaging (DWI) modality and its effect on the detection of clinically significant prostate cancer (csPCa) and prostatitis using deep learning, as the optimal range is not yet clearly defined in this domain. Next, we propose a simulated multimodal shift as a data augmentation technique to compensate for the multimodal shift present in the data. Third, we study the effect of incorporating the prostatitis class alongside cancer-related findings at three different granularities of the prostate cancer class (coarse, medium, and fine) and its impact on the detection rate of the target csPCa. Furthermore, ordinal and one-hot encoded (OHE) output formulations were tested. RESULTS An optimal model configuration with fine class granularity (prostatitis included) and OHE has scored the lesion-wise partial Free-Response Receiver Operating Characteristic (FROC) area under the curve (AUC) of 1.94 (CI 95%: 1.76-2.11) and patient-wise ROC AUC of 0.874 (CI 95%: 0.793-0.938) in the detection of csPCa. Inclusion of the auxiliary prostatitis class has demonstrated a stable relative improvement in specificity at a false positive rate (FPR) of 1.0 per patient, with an increase of 3%, 7%, and 4% for coarse, medium, and fine class granularities. CONCLUSIONS This paper examines several configurations for model training in the biparametric MRI setup and proposes optimal value ranges. It also shows that the fine-grained class configuration, including prostatitis, is beneficial for detecting csPCa. The ability to detect prostatitis in all low-risk cancer lesions suggests the potential to improve the quality of the early diagnosis of prostate diseases. It also implies an improved interpretability of the results by the radiologist.
Collapse
Affiliation(s)
- Oleksii Bashkanov
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany.
| | - Marko Rak
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
| | - Anneke Meyer
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
| | | | | | | | - Christian Hansen
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
| |
Collapse
|
3
|
Liu YF, Shu X, Qiao XF, Ai GY, Liu L, Liao J, Qian S, He XJ. Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer. Front Oncol 2022; 12:911426. [PMID: 35795067 PMCID: PMC9252170 DOI: 10.3389/fonc.2022.911426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/19/2022] [Indexed: 01/31/2023] Open
Abstract
Objective To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). Methods A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy. Results A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900). Conclusions The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.
Collapse
Affiliation(s)
- Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Liu
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Jun Liao
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Shuang Qian
- Big Data and Software Engineering College, Chongqing University, Chongqing, China
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Xiao-Jing He,
| |
Collapse
|
4
|
Prabhu S, Prasad K, Robels-Kelly A, Lu X. AI-based carcinoma detection and classification using histopathological images: A systematic review. Comput Biol Med 2022; 142:105209. [DOI: 10.1016/j.compbiomed.2022.105209] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 02/07/2023]
|
5
|
Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM, Nolan GP. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 2021; 16:3802-3835. [PMID: 34215862 PMCID: PMC8647621 DOI: 10.1038/s41596-021-00556-8] [Citation(s) in RCA: 218] [Impact Index Per Article: 72.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 04/14/2021] [Indexed: 02/06/2023]
Abstract
Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity.
Collapse
Affiliation(s)
- Sarah Black
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Darci Phillips
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Julia Kennedy-Darling
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Akoya Biosciences, Menlo Park, CA, USA
| | - Vishal G Venkataraaman
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolay Samusik
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Becton Dickinson, San Jose, CA, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
6
|
Roszkowiak L, Korzynska A, Siemion K, Zak J, Pijanowska D, Bosch R, Lejeune M, Lopez C. System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL). Sci Rep 2021; 11:9291. [PMID: 33927266 PMCID: PMC8085130 DOI: 10.1038/s41598-021-88611-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 04/14/2021] [Indexed: 02/02/2023] Open
Abstract
This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.
Collapse
Affiliation(s)
- Lukasz Roszkowiak
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland.
| | - Anna Korzynska
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Krzysztof Siemion
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
- Medical Pathomorphology Department, Medical University of Bialystok, Białystok, Poland
| | - Jakub Zak
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Dorota Pijanowska
- Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Ramon Bosch
- Pathology Department, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
| | - Marylene Lejeune
- Molecular Biology and Research Section, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
| | - Carlos Lopez
- Molecular Biology and Research Section, Hospital de Tortosa Verge de la Cinta, Institut d'Investigacio Sanitaria Pere Virgili (IISPV), URV, Tortosa, Spain
| |
Collapse
|