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Diagnostic methods for detection of bone metastases.

ABSTRACT: Skeletal metastases are severe complications in the course of cancer, and they indicate a worse prognosis. The use of modern imaging techniques allows rapid diagnosis of bone metastases. Properly selected diagnostic imaging (scintigraphy, positron emission tomography, whole body MRI) allows us to evaluate the number of metastatic foci in the skeletal system. Complementary imaging examinations (X-ray, computed tomography, magnetic resonance imaging) determine the extent of metastasis and its character: osteolytic, osteoblast, mixed). Hypercalcaemia is a symptom of low specificity for metastatic bone disease (a result of osteolysis); nevertheless, it is a significant complication in oncological treatment and worsens the prognosis of the patient. A biopsy is the final stage of the diagnostic process, which allows us to assess cell and tissue changes. Guided biopsies are performed under the control of musculoskeletal imaging methods (CT, MRI) and they are the most promising tools in bone metastases diagnosis. The development of guided biopsy techniques has led to the conclusion that they should be standard in diagnosing bone metastases. Liquid biopsy (LB) seems to be the most promising diagnostic method for detection of bone metastases. LB based on tumour-specific DNA mutation gives an opportunity for early detection and assessment of the molecular heterogeneity of the overall disease.

SUBMITTER: Lukaszewski B

PROVIDER: S-EPMC5611498 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Diagnostic methods for detection of bone metastases.

Łukaszewski Bartosz B Nazar Jerzy J Goch Maciej M Łukaszewska Marta M Stępiński Adrian A Jurczyk Mieczysława U MU

Contemporary oncology (Poznan, Poland) 20170630 2

Skeletal metastases are severe complications in the course of cancer, and they indicate a worse prognosis. The use of modern imaging techniques allows rapid diagnosis of bone metastases. Properly selected diagnostic imaging (scintigraphy, positron emission tomography, whole body MRI) allows us to evaluate the number of metastatic foci in the skeletal system. Complementary imaging examinations (X-ray, computed tomography, magnetic resonance imaging) determine the extent of metastasis and its char ...[more]

PMID: 28947878

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Project description:Bone metastasis is a major cause of morbidity within solid tumours of the breast, prostate, lung and kidney. Metastasis to the skeleton is associated with a wide range of complications including bone fractures, spinal cord compression, hypercalcaemia and increased bone pain. Improved treatments for bone metastasis, such as the use of anti-bone resorptive bisphosphonate agents, within post-menopausal women have improved disease-free survival; however, these treatments are not without side effects. There is thus a need for biomarkers, which will predict the risk of developing the spread to bone within these cancers. The application of molecular profiling techniques, together with animal model systems and engineered cell-lines has enabled the identification of a series of potential bone-metastasis biomarker molecules predictive of bone metastasis risk. Some of these biomarker candidates have been validated within patient-derived samples providing a step towards clinical utility. Recent developments in multiplex biomarker quantification now enable the simultaneous measurement of up to 96 micro-RNA/protein molecules in a spatially defined manner with single-cell resolution, thus enabling the characterisation of the key molecules active at the sites of pre-metastatic niche formation as well as tumour-stroma signalling. These technologies have considerable potential to inform biomarker discovery. Additionally, a potential future extension of these discoveries could also be the identification of novel drug targets within cancer spread to bone. This chapter summarises recent findings in biomarker discovery within the key bone metastatic cancers (breast, prostate, lung and renal cell carcinoma). Tissue-based and circulating blood-based biomarkers are discussed from the fields of genomics, epigenetic regulation (micro-RNAs) and protein/cell-signalling together with a discussion of the potential future development of these markers towards clinical development.

Project description:Purpose To estimate the ability of lower dose levels and iterative reconstruction (IR) to display hepatic metastases that can be detected by radiologists. Materials and Methods Projection data from 83 contrast agent-enhanced CT examinations were collected. Metastases were defined by histopathologic analysis or progression and regression. Lower radiation dose configurations were reconstructed at five dose levels with filtered back projection (FBP) and IR (automatic exposure control settings: 80, 100, 120, 160, and 200 quality reference mAs [QRM]). Three abdominal radiologists circumscribed metastases, indicating confidence (confidence range, 0-100) and image quality. Noninferiority was assessed by using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis (noninferiority limit, -0.10) and reader agreement rules, which required identification of metastases identified at routine dose, and no nonlesion localizations in patients negative for metastases, in 71 or more patient CT examinations (of 83), for each configuration. Results There were 123 hepatic metastases (mean size, 1.4 cm; median volume CT dose index and size-specific dose estimate, 11.0 and 13.4 mGy, respectively). By using JAFROC figure of merit, 100 QRM FBP did not meet noninferiority criteria and had estimated performance difference from routine dose of -0.08 (95% confidence interval: -0.11, -0.04). Preset reader agreement rules were not met for 100 QRM IR or 80 QRM IR, but were met for doses 120 QRM or higher (ie, size-specific dose estimate ? 8.0 mGy). IR improved image quality (P < .05) but not reader performance. Other than 160 QRM IR, lower dose levels were associated with reduced confidence in metastasis detection (P < .001). Conclusion For detection of hepatic metastases by using contrast-enhanced CT, dose levels that corresponded to 120 quality reference mAs (size-specific dose estimate, 8.0 mGy) and higher performed similarly to 200 quality reference mAs with filtered back projection. © RSNA, 2018 Online supplemental material is available for this article.

Project description:ImportanceApplication of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.ObjectiveAssess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.Design, setting, and participantsResearcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).ExposuresDeep learning algorithms submitted as part of a challenge competition or pathologist interpretation.Main outcomes and measuresThe presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.ResultsThe area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).Conclusions and relevanceIn the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

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Diagnostic methods for detection of bone metastases.

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Diagnostic methods for detection of bone metastases.

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