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OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.
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Inteligência Artificial , Neoplasias da Mama , Meios de Contraste , Mamografia , Humanos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Feminino , Pessoa de Meia-Idade , Mamografia/métodos , Idoso , Itália , Adulto , Gradação de Tumores , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Receptor ErbB-2 , Sensibilidade e Especificidade , RadiômicaRESUMO
Breast cancer remains a significant global concern, underscoring the critical need for early detection and prevention strategies. Primary and secondary preventive measures, such as routine screenings and behaviors like breast self-examination (BSE), play a crucial role in facilitating early diagnosis. While the National Health System (NHS) in Italy offers free regular screenings for women aged 50-69, there is a lack of clarity regarding the participation of both Italian and Chinese women residing in Italy in these screening programs. This study aims to bridge this knowledge gap by thoroughly assessing the involvement in regular clinical check-ups and the types of screening employed, the adherence to free screenings offered by the NHS, and the practice of BSE among women aged 50-69 of these two groups. Furthermore, it investigates their knowledge and perceptions regarding breast cancer and BSE. Results reveal disparities in breast cancer control practice between Italian and Chinese women in Italy: the former demonstrates higher adherence to clinical checkups (53% vs. 3%, p < 0.001), while both groups show low participation in free NHS screenings (70% vs. 4%, p < 0.001). Additionally, Chinese women reported significantly lower frequency of mammography (96% vs. 33%, p < 0.001) and ultrasound (69% vs. 16%, p < 0.001). The frequency of BSE also differed substantially, with 47% of Chinese women never performing BSE compared to 12% of Italian women (p < 0.001). This comprehensive exploration provides valuable insights, attitudes, and knowledge into the disparities and potential areas for improvement in breast cancer prevention, thus contributing to the overall well-being of these communities. The findings highlight the necessity for educational initiatives aimed at improving awareness and participation in screenings, particularly among the Chinese population. These initiatives could have profound implications for patient education by equipping women with the knowledge and skills necessary to engage in proactive health behaviors.
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Hospitalization for breast surgery is a distressing experience for women. This study investigated the impact of music therapy (MT), an integrative approach that is characterized by the establishment of a therapeutic relationship between patients and a certified music therapist, through different musical interventions targeted to the specific needs of the patients. The impact of two different MT experiences was compared on anxiety and distressing emotions. METHODS: One hundred fifty-one patients during hospitalization for breast surgery were randomly assigned to two music therapy treatment arms: individual/receptive (MTri) vs. group/active-receptive integrated (MTiGrp). Stress, depression, anger, and need for help were measured with the emotion thermometers (ET) and State Trait Anxiety Inventory Y-1 form (STAY-Y1). Data were collected before and after the MT intervention. RESULTS: Both types of MT interventions were effective in reducing all the variables: stress, depression, anger, and anxiety (T Student p<0.01). Patients' perception of help received was correlated with a significant reduction in anxiety and distressing emotions during hospitalization for breast surgery. CONCLUSION: Considerations regarding the implementation of MT interventions in clinical practice are discussed. In individual receptive MT, there was a significant decrease in anxiety levels, whereas in the integrated MT group, there was a higher perception of help received and use of inter-individual resources.
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Neoplasias da Mama , Musicoterapia , Música , Humanos , Feminino , Música/psicologia , Neoplasias da Mama/cirurgia , Neoplasias da Mama/psicologia , Estresse Psicológico/etiologia , Estresse Psicológico/terapia , Estresse Psicológico/psicologia , Emoções , Ansiedade/etiologia , Ansiedade/terapia , Ansiedade/psicologiaRESUMO
BACKGROUND: A timely diagnosis is essential for improving breast cancer patients' survival and designing targeted therapeutic plans. For this purpose, the screening timing, as well as the related waiting lists, are decisive. Nonetheless, even in economically advanced countries, breast cancer radiology centres fail in providing effective screening programs. Actually, a careful hospital governance should encourage waiting lists reduction programs, not only for improving patients care, but also for minimizing costs associated with the treatment of advanced cancers. Thus, in this work, we proposed a model to evaluate several scenarios for an optimal distribution of the resources invested in a Department of Breast Radiodiagnosis. MATERIALS AND METHODS: Particularly, we performed a cost-benefit analysis as a technology assessment method to estimate both costs and health effects of the screening program, to maximise both benefits related to the quality of care and resources employed by the Department of Breast Radiodiagnosis of Istituto Tumori "Giovanni Paolo II" of Bari in 2019. Specifically, we determined the Quality-Adjusted Life Year (QALY) for estimating health outcomes, in terms of usefulness of two hypothetical screening strategies with respect to the current one. While the first hypothetical strategy adds one team made up of a doctor, a technician and a nurse, along with an ultrasound and a mammograph, the second one adds two afternoon teams. RESULTS: This study showed that the most cost-effective incremental ratio could be achieved by reducing current waiting lists from 32 to 16 months. Finally, our analysis revealed that this strategy would also allow to include more people in the screening programs (60,000 patients in 3 years).
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Neoplasias da Mama , Radiologia , Humanos , Feminino , Análise Custo-Benefício , Listas de Espera , MamografiaRESUMO
Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.
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Neoplasias da Mama , Detecção Precoce de Câncer , Feminino , Humanos , Detecção Precoce de Câncer/métodos , Estudos Retrospectivos , Mama/diagnóstico por imagem , Mamografia/métodos , Neoplasias da Mama/patologia , Biópsia Guiada por Imagem/métodos , Biópsia com Agulha de Grande CalibreRESUMO
OBJECTIVE: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS: The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Inteligência Artificial , Imageamento por Ressonância Magnética/métodos , Estudos RetrospectivosRESUMO
BACKGROUND: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. RESULTS: For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. CONCLUSIONS: The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.
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Mama , Calcinose/diagnóstico , Aprendizado de Máquina , Algoritmos , Área Sob a Curva , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico , Bases de Dados Factuais , Feminino , Humanos , Mamografia , Curva ROCRESUMO
OBJECTIVE: The aim of this study is to present a 6-year prospective evaluation of second-look ultrasound (US) using volume navigation (V Nav) for MRI-detected additional breast lesions. METHODS: After IRB approvals in both institutions, 1930 consecutive prone MRI breast examinations in 1437 patients were prospectively evaluated. All patients with an MRI-detected additional lesion underwent second-look US, and if occult, contrast-enhanced MRI in supine position was performed for US and MRI co-registration. For patients with breast hypertrophy, MRI-guided biopsy was performed directly. Pathologic examination was the standard of reference. One-way ANOVA and chi-square tests were used. RESULTS: In 490 MRI examinations (25.4%, 490/1930), at least one additional breast lesion was detected for a total of 722 only MRI-detected lesions. Second-look US identified 549 additional lesions (23 ± 8 mm); 362 (65.9%, 362/549) proved benign at pathology and 187 (34.1%, 187/549) malignant. Second-look US with V Nav identified 151 additional lesions (17 ± 9 mm, p = n.s.); 67 (44.4%, 67/151) proved benign at pathology and 84 (55.6%, 84/151) malignant. MRI-guided biopsy was performed on 22 additional breast lesions (22 ± 8 mm, p = n.s.); pathology revealed 20 (90.9%, 20/22) benign lesions and 2 (9.1%, 2/22) malignant ones. Mass lesions were significantly higher in the second-look US group (p < 0.001). No significant difference in lesion dimension was found between the three groups (p = 0.729). CONCLUSIONS: Second-look US with V Nav can be effective in detecting a large number of additional breast lesions occult at second-look US and to biopsy a significant number of malignant lesions safely and irrespective of distance from skin or lesion position. KEY POINTS: ⢠Second-look US with volume navigation is effective in detecting occult additional lesions. ⢠Permits safe biopsies irrespective of position and depth ⢠Reduces the need for MRI-guided biopsy.
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Neoplasias da Mama/diagnóstico por imagem , Ultrassonografia Mamária/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias da Mama/patologia , Meios de Contraste , Feminino , Seguimentos , Humanos , Biópsia Guiada por Imagem/métodos , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Imagem Multimodal/métodos , Estudos Prospectivos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Adulto JovemRESUMO
BACKGROUND: Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer's disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance. MATERIALS AND METHODS: The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available. RESULTS: HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC1 = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC2 = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans. CONCLUSIONS: The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer's disease and become a useful tool for other neuroimaging applications.
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Algoritmos , Doença de Alzheimer/diagnóstico , Doença de Alzheimer/patologia , Hipocampo/diagnóstico por imagem , Hipocampo/patologia , Processamento de Imagem Assistida por Computador , Idoso , Doença de Alzheimer/complicações , Doença de Alzheimer/diagnóstico por imagem , Atrofia/complicações , Feminino , Humanos , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Masculino , Tamanho do ÓrgãoRESUMO
BACKGROUND: International guidelines recommend a pathway for preferable nursing handling in a specific cancer topic, like chemotherapy toxicity, low adhesion in toxicity reported with a consequential increase in adverse events (AEs) frequency, poorer QoL outcomes, and increased use of healthcare service until death. Unpredictability, postponed reports, and incapability to access healthcare services can compromise toxicity-related effects by including patients' safety. In this scenario, a more attentive nursing intervention can improve patients' outcomes and decrease costs for healthcare services, respectively. The present scoping review aims to describe and synthesize scientific care nursing evidence assessment in oncology patients. METHODS: PubMed, Embase, Nursing & Allied Health Database, and British Nursing were the databases examined. Keywords used and associated with Boolean operators were assessment, care, nursing, immune disorder, oncology, and patient. Research articles considered were published between 2013-2023. All systematic processes were performed according to the PRISMA procedure in order to reach all manuscripts considered in the present scoping review. RESULTS: The Embase database showed a total of 25 articles, PubMed displayed 77, the Nursing & Allied Health Database evidenced a total of 74, and the British Nursing database showed 252 records. Then, after a first revision in each database by considering the inclusion criteria, the abovementioned titles and abstracts were selected and, 336 records were removed, and 92 studies remained. Of these, 65 manuscripts were excluded after verifying abstracts. Finally, a total of 7 articles were carefully analysed and selected for this scoping review. Specifically, 2 articles belonged to the British Nursing Database, 3 articles belonged to Embase, 1 to the Nursing & Allied Health Database and one related to PubMed. CONCLUSION: Oncology nursing should consider several aspects, such as therapy-related toxicity and its related morbidity and mortality, worsening levels of quality of life, and increasing duty by the healthcare organization or endorsements for the principal symptoms and signs which may anticipate few diseases and worst clinical conditions, too. Therefore, careful monitoring may allow prompt recognition and subsequent earlier management in the treatment efficacy.
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BACKGROUND: Breast cancer remains a significant health concern among women globally. Despite advancements in awareness and diagnostic techniques, it persists as a leading cause of death, with profound impacts on affected individuals' quality of life. Primary and secondary prevention, including regular screenings and practices like breast self-examination (BSE), are pivotal in ensuring early diagnosis. The national health system (NHS) in Italy offers screenings for women aged 50-69 every two years, managed by the local health authority. However, the participation rates, especially among the Chinese female population residing in Italy, are not well understood. METHODS: Using a snowball method, we electronically disseminated a survey to investigate how Chinese women living in Italy engage with available NHS screening programs. The survey also explores their practice of BSE and the use and impact of technological tools on prevention. Furthermore, the study aims to understand the subjects' depth of knowledge and misconceptions about breast cancer. RESULTS: The data reveal a significant gap in breast cancer screening adherence and knowledge among Chinese women in Italy, with a notable discrepancy between the general population and those who have previously encountered cancer. CONCLUSIONS: The results highlight the urgent need for interventions that are culturally sensitive, stressing that these actions are not only desirable but essential.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/prevenção & controle , Neoplasias da Mama/epidemiologia , Autoexame de Mama/métodos , Detecção Precoce de Câncer , Qualidade de Vida , Conhecimentos, Atitudes e Prática em Saúde , Estudos Transversais , Fatores de Risco , Inquéritos e Questionários , ChinaRESUMO
BACKGROUND: The suffering associated with a cancer diagnosis can find different channels to express itself: sleep disorders, psychiatric disorders, sexuality. These are not always analyzed by health professionals, but they have an impact on the patient's quality of life and on the outcome of the disease. METHODS: An observational study was conducted in order to investigate attitudes, knowledge and clinical practice towards psychological symptoms in cancer patients. RESULTS: A total of 132 clinicians from all Italian regions responded. In total, 99.2% (n = 131) considered the figure of the psychologist useful in the oncology field and recommended him/her in clinical practice (n = 115; 87.7%), especially in the terminal phase of the illness (58.6%; n = 99). Despite the importance given to the figure of the psychologist, psychiatric disorders are not diagnosed. Only 20.0% (n = 26) identified depressive disorder as accurate and only 33.9% (n = 43) identified demoralization syndrome as accurate. CONCLUSIONS: Results prove the need for training on psychological disorders in oncology and the emotional repercussions of cancer illness.
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Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV-) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV-), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice.
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Redes Neurais de Computação , Neoplasias Orofaríngeas , Infecções por Papillomavirus , Tomografia Computadorizada por Raios X , Humanos , Neoplasias Orofaríngeas/virologia , Neoplasias Orofaríngeas/diagnóstico por imagem , Neoplasias Orofaríngeas/patologia , Tomografia Computadorizada por Raios X/métodos , Infecções por Papillomavirus/diagnóstico por imagem , Infecções por Papillomavirus/virologia , Infecções por Papillomavirus/patologia , Masculino , Feminino , Papillomaviridae , Pessoa de Meia-Idade , Idoso , Carcinoma de Células Escamosas/diagnóstico por imagem , Carcinoma de Células Escamosas/virologia , Carcinoma de Células Escamosas/patologia , Carcinoma de Células Escamosas de Cabeça e Pescoço/virologia , Carcinoma de Células Escamosas de Cabeça e Pescoço/diagnóstico por imagem , Carcinoma de Células Escamosas de Cabeça e Pescoço/patologia , Carga Tumoral , Papillomavirus HumanoRESUMO
BACKGROUND: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black-boxes due to the difficulty of understanding the decision-making process used by the algorithm to obtain a specific result. AIMS: For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis. MATERIALS & METHODS: Since the diagnostic task was a three-class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme. RESULTS: The accuracy of the three-class model reaches an overall accuracy of 86.36%, and the precision per-class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. DISCUSSION: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system. CONCLUSIONS: This is the first work that attempts to design an explainable machine-learning tool for the histological diagnosis of solid masses of the ovary.
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Doenças dos Anexos , Aprendizado de Máquina , Neoplasias Ovarianas , Ultrassonografia , Humanos , Feminino , Ultrassonografia/métodos , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/patologia , Neoplasias Ovarianas/diagnóstico , Pessoa de Meia-Idade , Adulto , Doenças dos Anexos/diagnóstico por imagem , Doenças dos Anexos/patologia , Idoso , Algoritmos , Diagnóstico DiferencialRESUMO
BACKGROUND: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. METHOD: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. RESULTS: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. CONCLUSION: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.
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Neoplasias da Mama , Terapia Neoadjuvante , Humanos , Feminino , Terapia Neoadjuvante/métodos , Inteligência Artificial , Meios de Contraste/uso terapêutico , Resultado do Tratamento , Imageamento por Ressonância Magnética/métodos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologiaRESUMO
Recent years have observed the emergence of novel therapeutic opportunities for advanced hepatocellular carcinoma (HCC), such as combination therapies including immune checkpoint inhibitors. We performed a meta-analysis with the aim to compare median overall survival (OS), median progression-free survival (PFS), complete response (CR) rate, and partial response (PR) rate in advanced HCC patients receiving immune-based combinations versus sorafenib. A total of 2176 HCC patients were available for the meta-analysis (immune-based combinations = 1334; sorafenib = 842) and four trials were included. Immune-based combinations decreased the risk of death by 27% (HR, 0.73; 95% CI, 0.65−0.83; p < 0.001); similarly, a PFS benefit was observed (HR, 0.64; 95% CI, 0.5−0.84; p < 0.001). In addition, immune-based combinations showed better CR rate and PR rate, with ORs of 12.4 (95% CI, 3.02−50.85; p < 0.001) and 3.48 (95% CI, 2.52−4.8; p < 0.03), respectively. The current study further confirms that first-line immune-based combinations have a place in the management of HCC. The CR rate observed in HCC patients receiving immune-based combinations appears more than twelve times higher compared with sorafenib monotherapy, supporting the long-term benefit of these combinatorial strategies, with even the possibility to cure advanced disease.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Sorafenibe/uso terapêutico , Carcinoma Hepatocelular/tratamento farmacológico , Neoplasias Hepáticas/tratamento farmacológico , Intervalo Livre de Progressão , Terapia CombinadaRESUMO
BACKGROUND: The incidental detection of a right atrial mass during routine cardioncological workup is a rare condition. The correct differential diagnosis between cancer and thrombi is challenging. A biopsy may not be feasible while diagnostic techniques and tools may not be available. CASE SUMMARY: We report the case of a 59-year-old female patient with a history of breast cancer and current secondary metastatic pancreatic cancer. She developed deep vein thrombosis and pulmonary embolism and was admitted to the Outpatient Clinic of our Cardio-Oncology Unit for follow-up. Transthoracic echocardiogram incidentally found a right atrial mass. Clinical management was difficult due to the abrupt worsening of the patient's clinical condition and the progressive severe thrombocytopenia. We suspected a thrombus, according to its echocardiographic appearance, the patient's cancer history and recent venous thromboembolism. The patient was unable to adhere to low molecular weight heparin treatment. Due to worsening prognosis, palliative care was recommended. We also highlighted the distinguishing features between thrombi and tumors. We proposed a diagnostic flowchart to aid diagnostic decision making in the case of an incidental atrial mass. CONCLUSION: This case report highlights the importance of cardioncological surveillance during anticancer treatments to detect cardiac masses.
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Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/genética , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/genética , Tomografia Computadorizada por Raios X/métodos , Aprendizado de MáquinaRESUMO
BACKGROUND: About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features. METHOD: First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. RESULTS: The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. CONCLUSION: Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.
Assuntos
Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Prognóstico , Terapia Neoadjuvante/métodos , Estudos Retrospectivos , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismo , Trastuzumab/uso terapêutico , Aprendizado de Máquina , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêuticoRESUMO
Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30-55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem.