Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing coronavirus disease 2019(COVID-19),can trigger autoimmunity in genetically predisposed individuals through hyperstimulation of immune response and mo...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing coronavirus disease 2019(COVID-19),can trigger autoimmunity in genetically predisposed individuals through hyperstimulation of immune response and molecular mimicry.Here we summarise the current knowledge about autoimmune liver diseases(AILDs)and SARS-CoV-2,focusing on:(1)The risk of SARS-CoV-2 infection and the course of COVID-19 in patients affected by AILDs;(2)the role of SARS-CoV-2 in inducing liver damage and triggering AILDs;and(3)the ability of vaccines against SARS-CoV-2 to induce autoimmune responses in the liver.Data derived from the literature suggest that patients with AILDs do not carry an increased risk of SARS-Cov-2 infection but may develop a more severe course of COVID-19 if on treatment with steroids or thiopurine.Although SARSCoV-2 infection can lead to the development of several autoimmune diseases,few reports correlate it to the appearance of de novo manifestation of immunemediated liver diseases such as autoimmune hepatitis(AIH),primary biliary cholangitis(PBC)or AIH/PBC overlap syndrome.Different case series of an AIHlike syndrome with a good prognosis after SARS-CoV-2 vaccination have been described.Although the causal link between SARS-CoV-2 vaccines and AIH cannot be definitively established,these reports suggest that this association could be more than coincidental.展开更多
In patients with colorectal liver metastasis(CRLMs)unsuitable for surgery,oncological treatments,such as chemotherapy and targeted agents,can be performed.Cross-sectional imaging[computed tomography(CT),magnetic reson...In patients with colorectal liver metastasis(CRLMs)unsuitable for surgery,oncological treatments,such as chemotherapy and targeted agents,can be performed.Cross-sectional imaging[computed tomography(CT),magnetic resonance imaging(MRI),18-fluorodexoyglucose positron emission tomography with CT/MRI]evaluates the response of CRLMs to therapy,using post-treatment lesion shrinkage as a qualitative imaging parameter.This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them.Consequently,there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients.Advanced quantitative imaging(diffusionweighted imaging,perfusion imaging,molecular imaging)allows the in vivo evaluation of specific biological tissue features described as quantitative parameters.Furthermore,radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters.As a result,parametric analysis(PA)translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion,structural heterogeneity,cellularity,oxygenation,and glucose consumption.PA could be a potentially useful imaging marker for predicting CRLMs treatment response.This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.展开更多
The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal c...The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.展开更多
Colorectal cancer is one of the few malignant tumors in which synchronous or metachronous liver metastases [colorectal liver metastases(CRLMs)] may be treated with surgery. It has been demonstrated that resection of C...Colorectal cancer is one of the few malignant tumors in which synchronous or metachronous liver metastases [colorectal liver metastases(CRLMs)] may be treated with surgery. It has been demonstrated that resection of CRLMs improves the long-term prognosis. On the other hand, patients with un-resectable CRLMs may benefit from chemotherapy alone or in addition to liverdirected therapies. The choice of the most appropriate therapeutic management of CRLMs depends mostly on the diagnostic imaging. Nowadays, multiple non-invasive imaging modalities are available and those have a pivotal role in the workup of patients with CRLMs. Although extensive research has been performed with regards to the diagnostic performance of ultrasonography, computed tomography, positron emission tomography and magnetic resonance for the detection of CRLMs, the optimal imaging strategies for staging and follow up are still to be established. This largely due to the progressive technological and pharmacological advances which are constantly improving the accuracy of each imaging modality. This review describes the non-invasive imaging approaches of CRLMs reporting the technical features, the clinical indications, the advantages and the potential limitations of each modality, as well as including some information on the development of new imaging modalities, the role of new contrast media and the feasibility of using parametric image analysis as diagnostic marker of presence of CRLMs.展开更多
Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g.,...Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.展开更多
Pancreatic vipoma is an extremely rare tumor accounting for less than 2% of endocrine pancreatic neoplasms with a reported incidence of 0.1-0.6 per million. While cross-sectional imaging findings are usually not speci...Pancreatic vipoma is an extremely rare tumor accounting for less than 2% of endocrine pancreatic neoplasms with a reported incidence of 0.1-0.6 per million. While cross-sectional imaging findings are usually not specific, exact localization of the tumor by means of either computed tomography(CT) or magnetic resonance(MR) is pivotal for surgical planning. However, cross-sectional imaging findings are usually not specific and further characterization of the tumor may only be achieved bysomatostatin-receptor scintigraphy(SRS). We report the case of a 70 years old female with a two years history of watery diarrhoea who was found to have a solid, inhomogeneously enhancing lesion at the level of the pancreatic tail at Gadolinium-enhanced MR(Somatom Trio 3T, Siemens, Germany). The tumor had been prospectively overlooked at a contrast-enhanced multi-detector CT(Aquilion 64, Toshiba, Japan) performed after i.v. bolus injection of only 100 cc of iodinated non ionic contrast media because of a chronic renal failure(3.4 mg/mL) but it was subsequently confirmed by SRS. The patient first underwent a successful symptomatic treatment with somatostatin analogues and was then submitted to a distal pancreasectomy with splenectomy to remove a capsulated whitish tumor which turned out to be a well-differentiated vipoma at histological and immuno-histochemical analysis.展开更多
AIM:To describe the role of nuclear imaging modalities using nor-cholesterol,metaiodobenzylguanidine(MIBG)and fluorine-deoxy-glucose(FDG)in adrenal tumors for lesion characterization in comparison with magnetic resona...AIM:To describe the role of nuclear imaging modalities using nor-cholesterol,metaiodobenzylguanidine(MIBG)and fluorine-deoxy-glucose(FDG)in adrenal tumors for lesion characterization in comparison with magnetic resonance(MR).METHODS:Population was classified in group 1 consisting of 30 patients with non-hypersecreting unilateral adrenal masses,in group 2 consisting of 34 patients with hypersecreting(n=19)or non-hypersecreting(n=15)adrenal adenomas and in group 3 consisting of 18 patients with chromaffin-tissue tumors(CTT),of which 14 were pheochromocytomas while 4 were paragangliomas(n=4).All patients underwent MR and nuclear studies(nor-cholesterol,MIBG and FDG).Pathology samples(n=63)or follow-up data in adenomas(n=19)were used as standard of reference forimaging studies interpretation.RESULTS:In group 1,MR findings were not highly accurate for lesion characterization,while the results of nuclear scans showed abnormal nor-cholesterol,MIBG and FDG concentration in all cases of adenomas,pheos and malignant tumors,respectively.In group 2,no differences in MR parameters were found between hyperfunctioning and non-hyperfunctioning adenomas,while nor-cholesterol uptake was significantly higher in hyperfunctioning compared to non-hyperfunctioning lesions.In group 3,no differences in MR parameters were found between benign and malignant CCT,while MIBG uptake was significantly higher in malignant compared to benign tumors.CONCLUSION:On the basis of our findings,nuclear imaging modalities using specific target agents are able to better characterize adrenal tumors,compared with MR.In particular,radionuclide techniques are able to identify the nature of adrenal incidentalomas and to differentiate between hypersecreting and non-hypersecreting adenomas as well as between benign and malignant CTT.展开更多
In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clini...In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clinical and imaging cancer workflow.The growing amounts of available“big data”coupled to the increasing computational power have enabled the development of computerbased systems capable to perform advanced tasks in many areas of clinical care,especially in medical imaging.ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions.DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks;convolutional neural networks are the most common DL models used in medical image analysis.AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications.In detail,we explored the emerging applications of AI in oncological risk assessment,lesion detection,characterization,staging,and therapy response.Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice.Nevertheless,AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.展开更多
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing coronavirus disease 2019(COVID-19),can trigger autoimmunity in genetically predisposed individuals through hyperstimulation of immune response and molecular mimicry.Here we summarise the current knowledge about autoimmune liver diseases(AILDs)and SARS-CoV-2,focusing on:(1)The risk of SARS-CoV-2 infection and the course of COVID-19 in patients affected by AILDs;(2)the role of SARS-CoV-2 in inducing liver damage and triggering AILDs;and(3)the ability of vaccines against SARS-CoV-2 to induce autoimmune responses in the liver.Data derived from the literature suggest that patients with AILDs do not carry an increased risk of SARS-Cov-2 infection but may develop a more severe course of COVID-19 if on treatment with steroids or thiopurine.Although SARSCoV-2 infection can lead to the development of several autoimmune diseases,few reports correlate it to the appearance of de novo manifestation of immunemediated liver diseases such as autoimmune hepatitis(AIH),primary biliary cholangitis(PBC)or AIH/PBC overlap syndrome.Different case series of an AIHlike syndrome with a good prognosis after SARS-CoV-2 vaccination have been described.Although the causal link between SARS-CoV-2 vaccines and AIH cannot be definitively established,these reports suggest that this association could be more than coincidental.
文摘In patients with colorectal liver metastasis(CRLMs)unsuitable for surgery,oncological treatments,such as chemotherapy and targeted agents,can be performed.Cross-sectional imaging[computed tomography(CT),magnetic resonance imaging(MRI),18-fluorodexoyglucose positron emission tomography with CT/MRI]evaluates the response of CRLMs to therapy,using post-treatment lesion shrinkage as a qualitative imaging parameter.This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them.Consequently,there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients.Advanced quantitative imaging(diffusionweighted imaging,perfusion imaging,molecular imaging)allows the in vivo evaluation of specific biological tissue features described as quantitative parameters.Furthermore,radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters.As a result,parametric analysis(PA)translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion,structural heterogeneity,cellularity,oxygenation,and glucose consumption.PA could be a potentially useful imaging marker for predicting CRLMs treatment response.This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.
文摘The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.
文摘Colorectal cancer is one of the few malignant tumors in which synchronous or metachronous liver metastases [colorectal liver metastases(CRLMs)] may be treated with surgery. It has been demonstrated that resection of CRLMs improves the long-term prognosis. On the other hand, patients with un-resectable CRLMs may benefit from chemotherapy alone or in addition to liverdirected therapies. The choice of the most appropriate therapeutic management of CRLMs depends mostly on the diagnostic imaging. Nowadays, multiple non-invasive imaging modalities are available and those have a pivotal role in the workup of patients with CRLMs. Although extensive research has been performed with regards to the diagnostic performance of ultrasonography, computed tomography, positron emission tomography and magnetic resonance for the detection of CRLMs, the optimal imaging strategies for staging and follow up are still to be established. This largely due to the progressive technological and pharmacological advances which are constantly improving the accuracy of each imaging modality. This review describes the non-invasive imaging approaches of CRLMs reporting the technical features, the clinical indications, the advantages and the potential limitations of each modality, as well as including some information on the development of new imaging modalities, the role of new contrast media and the feasibility of using parametric image analysis as diagnostic marker of presence of CRLMs.
文摘Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.
文摘Pancreatic vipoma is an extremely rare tumor accounting for less than 2% of endocrine pancreatic neoplasms with a reported incidence of 0.1-0.6 per million. While cross-sectional imaging findings are usually not specific, exact localization of the tumor by means of either computed tomography(CT) or magnetic resonance(MR) is pivotal for surgical planning. However, cross-sectional imaging findings are usually not specific and further characterization of the tumor may only be achieved bysomatostatin-receptor scintigraphy(SRS). We report the case of a 70 years old female with a two years history of watery diarrhoea who was found to have a solid, inhomogeneously enhancing lesion at the level of the pancreatic tail at Gadolinium-enhanced MR(Somatom Trio 3T, Siemens, Germany). The tumor had been prospectively overlooked at a contrast-enhanced multi-detector CT(Aquilion 64, Toshiba, Japan) performed after i.v. bolus injection of only 100 cc of iodinated non ionic contrast media because of a chronic renal failure(3.4 mg/mL) but it was subsequently confirmed by SRS. The patient first underwent a successful symptomatic treatment with somatostatin analogues and was then submitted to a distal pancreasectomy with splenectomy to remove a capsulated whitish tumor which turned out to be a well-differentiated vipoma at histological and immuno-histochemical analysis.
文摘AIM:To describe the role of nuclear imaging modalities using nor-cholesterol,metaiodobenzylguanidine(MIBG)and fluorine-deoxy-glucose(FDG)in adrenal tumors for lesion characterization in comparison with magnetic resonance(MR).METHODS:Population was classified in group 1 consisting of 30 patients with non-hypersecreting unilateral adrenal masses,in group 2 consisting of 34 patients with hypersecreting(n=19)or non-hypersecreting(n=15)adrenal adenomas and in group 3 consisting of 18 patients with chromaffin-tissue tumors(CTT),of which 14 were pheochromocytomas while 4 were paragangliomas(n=4).All patients underwent MR and nuclear studies(nor-cholesterol,MIBG and FDG).Pathology samples(n=63)or follow-up data in adenomas(n=19)were used as standard of reference forimaging studies interpretation.RESULTS:In group 1,MR findings were not highly accurate for lesion characterization,while the results of nuclear scans showed abnormal nor-cholesterol,MIBG and FDG concentration in all cases of adenomas,pheos and malignant tumors,respectively.In group 2,no differences in MR parameters were found between hyperfunctioning and non-hyperfunctioning adenomas,while nor-cholesterol uptake was significantly higher in hyperfunctioning compared to non-hyperfunctioning lesions.In group 3,no differences in MR parameters were found between benign and malignant CCT,while MIBG uptake was significantly higher in malignant compared to benign tumors.CONCLUSION:On the basis of our findings,nuclear imaging modalities using specific target agents are able to better characterize adrenal tumors,compared with MR.In particular,radionuclide techniques are able to identify the nature of adrenal incidentalomas and to differentiate between hypersecreting and non-hypersecreting adenomas as well as between benign and malignant CTT.
文摘In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clinical and imaging cancer workflow.The growing amounts of available“big data”coupled to the increasing computational power have enabled the development of computerbased systems capable to perform advanced tasks in many areas of clinical care,especially in medical imaging.ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions.DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks;convolutional neural networks are the most common DL models used in medical image analysis.AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications.In detail,we explored the emerging applications of AI in oncological risk assessment,lesion detection,characterization,staging,and therapy response.Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice.Nevertheless,AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.
