Automated Detection of Cribriform Growth Patterns in Prostate Histology Images

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than 0.0150 mm2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.Comment: 15 pages, 6 figure

Activation of c-MET Induces a Stem-Like Phenotype in Human Prostate Cancer

Prostate cancer consists of secretory cells and a population of immature cells. The function of immature cells and their mutual relation with secretory cells are still poorly understood. Immature cells either have a hierarchical relation to secretory cells (stem cell model) or represent an inducible population emerging upon appropriate stimulation of differentiated cells. Hepatocyte Growth Factor (HGF) receptor c-MET is specifically expressed in immature prostate cells. Our objective is to determine the role of immature cells in prostate cancer by analysis of the HGF/c-MET pathway

The clonal relation of primary upper urinary tract urothelial carcinoma and paired urothelial carcinoma of the bladder

The risk of developing urothelial carcinoma of the bladder (UCB) in patients treated by radical nephroureterectomy (RNU) for an upper urinary tract urothelial carcinoma (UTUC) is 22% to 47% in the 2 years after surgery. Subject of debate remains whether UTUC and the subsequent UCB are clonally related or represent separate origins. To investigate the clonal relationship between both entities, we performed targeted DNA sequencing of a panel of 41 genes on matched normal and tumor tissue of 15 primary UTUC patients treated by RNU who later developed 19 UCBs. Based on the detected tumor-specific DNA aberrations, the paired UTUC and UCB(s) of 11 patients (73.3%) showed a clonal relation, whereas in four patients the molecular results did not indicate a clear clonal relationship. Our results support the hypothesis that UCBs following a primary surgically resected UTUC are predominantly clonally derived recurrences and not separate entities

Gleason Grade 4 Prostate Adenocarcinoma Patterns: An Inter-observer Agreement Study among Genitourinary Pathologists

Aims To assess the interobserver reproducibility of individual Gleason grade 4 growth patterns. Methods and results Twenty-three genitourinary pathologists participated in the evaluation of 60 selected high-magnification photographs. The selection included 10 cases of Gleason grade 3, 40 of Gleason grade 4 (10 per growth pattern), and 10 of Gleason grade 5. Participants were asked to select a single predominant Gleason grade per case (3, 4, or 5), and to indicate the predominant Gleason grade 4 growth pattern, if present. ‘Consensus’ was defined as at least 80% agreement, and ‘favoured’ as 60–80% agreement. Consensus on Gleason grading was reached in 47 of 60 (78%) cases, 35 of which were assigned to grade 4. In the 13 non-consensus cases, ill-formed (6/13, 46%) and fused (7/13, 54%) patterns were involved in the disagreement. Among the 20 cases where at least one pathologist assigned the ill-formed growth pattern, none (0%, 0/20) reached consensus. Consensus for fused, cribriform and glomeruloid glands was reached in 2%, 23% and 38% of cases, respectively. In nine of 35 (26%) consensus Gleason grade 4 cases, participants disagreed on the growth pattern. Six of these were characterized by large epithelial proliferations with delicate intervening fibrovascular cores, which were alternatively given the designation fused or cribriform growth pattern (‘complex fused’). Conclusions Consensus on Gleason grade 4 growth pattern was predominantly reached on cribriform and glomeruloid patterns, but rarely on ill-formed and fused glands. The complex fused glands seem to constitute a borderline pattern of unknown prognostic significance on which a consensus could not be reached

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Echocardiographic prediction of outcome after cardiac resynchronization therapy: conventional methods and recent developments

Echocardiography plays an important role in patient assessment before cardiac resynchronization therapy (CRT) and can monitor many of its mechanical effects in heart failure patients. Encouraged by the highly variable individual response observed in the major CRT trials, echocardiography-based measurements of mechanical dyssynchrony have been extensively investigated with the aim of improving response prediction and CRT delivery. Despite recent setbacks, these techniques have continued to develop in order to overcome some of their initial flaws and limitations. This review discusses the concepts and rationale of the available echocardiographic techniques, highlighting newer quantification methods and discussing some of the unsolved issues that need to be addressed

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000

Colorectal liver metastases: Surgery versus thermal ablation (COLLISION) - a phase III single-blind prospective randomized controlled trial

Background: Radiofrequency ablation (RFA) and microwave ablation (MWA) are widely accepted techniques to eliminate small unresectable colorectal liver metastases (CRLM). Although previous studies labelled thermal ablation inferior to surgical resection, the apparent selection bias when comparing patients with unresectable disease to surgical candidates, the superior safety profile, and the competitive overall survival results for the more recent reports mandate the setup of a randomized controlled trial. The objective of the COLLISION trial is to prove non-inferiority of thermal ablation compared to hepatic resection in patients with at least one resectable and ablatable CRLM and no extrahepatic disease. Methods: In this two-arm, single-blind multi-center phase-III clinical trial, six hundred and eighteen patients with at least one CRLM (≤3cm) will be included to undergo either surgical resection or thermal ablation of appointed target lesion(s) (≤3cm). Primary endpoint is OS (overall survival, intention-to-treat analysis). Main secondary endpoints are overall disease-free survival (DFS), time to progression (TTP), time to local progression (TTLP), primary and assisted technique efficacy (PTE, ATE), procedural morbidity and mortality, length of hospital stay, assessment of pain and quality of life (QoL), cost-effectiveness ratio (ICER) and quality-adjusted life years (QALY). Discussion: If thermal ablation proves to be non-inferior in treating lesions ≤3cm, a switch in treatment-method may lead to a reduction of the post-procedural morbidity and mortality, length of hospital stay and incremental costs without compromising oncological outcome for patients with CRLM. Trial registration:NCT03088150 , January 11th 2017

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening