Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification

Brunese, Luca; Mercaldo, Francesco; Reginelli, Alfonso; Santone, Antonella

doi:10.1109/access.2019.2961754

Cited by 25 publications

(7 citation statements)

References 31 publications

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“…We consider four well-known metrics to evaluate the effectiveness of the model checking based approach, shown in Table 3. In reference 11 there are more details available.…”

Section: Resultsmentioning

confidence: 99%

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An Innovative Prostate Cancer Detection Method via Model Checking

Santone

Mercaldo

Brunese

et al. 2021

Preprint

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Purpose: one of typical cancer among men is the prostate tumour. This is the reason why the screening and the early detection is a crucial task to obtain a diagnosis and a subsequent therapy in the shortest possible time. Materials and Methods: in this paper, with the aim to help radiologists and pathologists for a prompt diagnosis, a method for detecting prostate cancer is proposed. Our analysis starts from the magnetic resonances images (coronal and axial planes), building a labelled transition system for coronal slices and another one for axial, which takes into account a number of non invasive radiomic features. Thus, a set of formulae in temporal logic characterizing the prostate cancer is verified through the model checking technique, to detect the prostate cancer. The proposed method considers magnetic resonance images without the Region Of Interest. This represents one of the major novelty of the method. Results: the proposed method is evaluated on a data-set composed of 40 patients, obtaining very interesting performances in the discrimination between affected and not affected prostate cancer patients. Conclusion: the study confirms the effectiveness of the formal methods to discriminate between cancerous and benign prostate MRIs with a method not requiring the ROI of the cancerous area, by obtaining a sensitivity and a specificity equal to 1.

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“…We consider four well-known metrics to evaluate the effectiveness of the model checking based approach, shown in Table 3. In reference 11 there are more details available.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, formal methods were applied from authors in 11,24 for the prostate cancer Gleason score detection. Differently from following proposal, methods in 11,24 require to pathologists to manually draw the ROI on the cancerous area to output the Gleason score.…”

Section: Discussionmentioning

confidence: 99%

An Innovative Prostate Cancer Detection Method via Model Checking

Santone

Mercaldo

Brunese

et al. 2021

Preprint

Self Cite

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“…Furthermore, we will explore if model checking can be considered to increase the novel coronavirus detection accuracy obtained by the proposed method: as a matter of fact, model checking already demonstrated their effectiveness in medical context as, for instance, the detection of prostate cancer Gleason score from computed tomography images 54 .…”

Section: Discussionmentioning

confidence: 99%

Coronavirus covid-19 detection by means of explainable deep learning

Mercaldo

Belfiore

Reginelli

et al. 2023

Sci Rep

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The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

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“…But, M Hennessy, et al [ 73 ] constructed the formal model, which was the mathematical algorithms formulated by the experts. The formal model was more interpretable than machine learning models and does not require large amounts of data for validation[ 74 ]. The formal models have been applied and have shown excellent predictive performance in previous studies [ 74 – 79 ].…”

Section: Discussionmentioning

confidence: 99%

“…The formal model was more interpretable than machine learning models and does not require large amounts of data for validation[ 74 ]. The formal models have been applied and have shown excellent predictive performance in previous studies [ 74 – 79 ]. However, the formal methods were not widely used and the predictive performance of the models needed to be further validated in the field of cancer.…”

Section: Discussionmentioning

confidence: 99%

Repeatability of radiomics studies in colorectal cancer: a systematic review

Liu

Wei

Xing³

et al. 2023

BMC Gastroenterol

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Background Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. Methods The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. Results A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. Conclusions The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.

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Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification

Cited by 25 publications

References 31 publications

An Innovative Prostate Cancer Detection Method via Model Checking

An Innovative Prostate Cancer Detection Method via Model Checking

Coronavirus covid-19 detection by means of explainable deep learning

Repeatability of radiomics studies in colorectal cancer: a systematic review

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