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1.
J Transl Med ; 22(1): 838, 2024 Sep 12.
Artículo en Inglés | MEDLINE | ID: mdl-39267101

RESUMEN

BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. METHODS: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). RESULTS: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. CONCLUSIONS: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.


Asunto(s)
Inteligencia Artificial , Melanoma , Ganglio Linfático Centinela , Humanos , Melanoma/patología , Melanoma/diagnóstico por imagen , Ganglio Linfático Centinela/patología , Ganglio Linfático Centinela/diagnóstico por imagen , Femenino , Masculino , Persona de Mediana Edad , Recurrencia Local de Neoplasia/patología , Anciano , Adulto , Reproducibilidad de los Resultados , Recurrencia , Curva ROC
2.
Bioinformatics ; 38(5): 1411-1419, 2022 02 07.
Artículo en Inglés | MEDLINE | ID: mdl-34864887

RESUMEN

MOTIVATION: In fluorescence microscopy, single-molecule localization microscopy (SMLM) techniques aim at localizing with high-precision high-density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super resolution plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. RESULTS: In this work, we propose a deep learning-based algorithm for precise molecule localization of high-density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its continuous exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data. AVAILABILITY AND IMPLEMENTATION: DeepCEL0 code is freely accessible at https://github.com/sedaboni/DeepCEL0.


Asunto(s)
Algoritmos , Imagen Individual de Molécula , Microscopía Fluorescente/métodos , Imagen Individual de Molécula/métodos , Colorantes Fluorescentes
3.
BMC Health Serv Res ; 23(1): 526, 2023 May 23.
Artículo en Inglés | MEDLINE | ID: mdl-37221516

RESUMEN

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).


Asunto(s)
Neoplasias de la Mama , Radiología , Humanos , Femenino , Análisis Costo-Beneficio , Listas de Espera , Mamografía
4.
PLoS Comput Biol ; 17(3): e1008870, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33784299

RESUMEN

The emerging tumor-on-chip (ToC) approaches allow to address biomedical questions out of reach with classical cell culture techniques: in biomimetic 3D hydrogels they partially reconstitute ex vivo the complexity of the tumor microenvironment and the cellular dynamics involving multiple cell types (cancer cells, immune cells, fibroblasts, etc.). However, a clear bottleneck is the extraction and interpretation of the rich biological information contained, sometime hidden, in the cell co-culture videos. In this work, we develop and apply novel video analysis algorithms to automatically measure the cytotoxic effects on human cancer cells (lung and breast) induced either by doxorubicin chemotherapy drug or by autologous tumor-infiltrating cytotoxic T lymphocytes (CTL). A live fluorescent dye (red) is used to selectively pre-stain the cancer cells before co-cultures and a live fluorescent reporter for caspase activity (green) is used to monitor apoptotic cell death. The here described open-source computational method, named STAMP (spatiotemporal apoptosis mapper), extracts the temporal kinetics and the spatial maps of cancer death, by localizing and tracking cancer cells in the red channel, and by counting the red to green transition signals, over 2-3 days. The robustness and versatility of the method is demonstrated by its application to different cell models and co-culture combinations. Noteworthy, this approach reveals the strong contribution of primary cancer-associated fibroblasts (CAFs) to breast cancer chemo-resistance, proving to be a powerful strategy to investigate intercellular cross-talks and drug resistance mechanisms. Moreover, we defined a new parameter, the 'potential of death induction', which is computed in time and in space to quantify the impact of dying cells on neighbor cells. We found that, contrary to natural death, cancer death induced by chemotherapy or by CTL is transmissible, in that it promotes the death of nearby cancer cells, suggesting the release of diffusible factors which amplify the initial cytotoxic stimulus.


Asunto(s)
Apoptosis/fisiología , Técnicas de Cocultivo/métodos , Linfocitos T Citotóxicos , Microambiente Tumoral/fisiología , Línea Celular Tumoral , Biología Computacional , Fibroblastos/citología , Fibroblastos/fisiología , Humanos , Cinética , Técnicas Analíticas Microfluídicas , Microscopía por Video , Linfocitos T Citotóxicos/citología , Linfocitos T Citotóxicos/fisiología
5.
Sensors (Basel) ; 20(5)2020 Mar 10.
Artículo en Inglés | MEDLINE | ID: mdl-32164292

RESUMEN

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.


Asunto(s)
Antineoplásicos/farmacología , Ensayos de Selección de Medicamentos Antitumorales , Próstata/efectos de los fármacos , Neoplasias de la Próstata/tratamiento farmacológico , Algoritmos , Fenómenos Biomecánicos , Movimiento Celular , Análisis por Conglomerados , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Aprendizaje Automático , Masculino , Microscopía , Modelos Estadísticos , Distribución Normal , Células PC-3 , Reconocimiento de Normas Patrones Automatizadas , Programas Informáticos , Grabación en Video
6.
Artículo en Inglés | MEDLINE | ID: mdl-39092734

RESUMEN

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.

7.
Cancer Med ; 13(12): e7425, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38923847

RESUMEN

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.


Asunto(s)
Enfermedades de los Anexos , Aprendizaje Automático , Neoplasias Ováricas , Ultrasonografía , Humanos , Femenino , Ultrasonografía/métodos , Neoplasias Ováricas/diagnóstico por imagen , Neoplasias Ováricas/patología , Neoplasias Ováricas/diagnóstico , Persona de Mediana Edad , Adulto , Enfermedades de los Anexos/diagnóstico por imagen , Enfermedades de los Anexos/patología , Anciano , Algoritmos , Diagnóstico Diferencial
8.
Healthcare (Basel) ; 12(15)2024 Aug 02.
Artículo en Inglés | MEDLINE | ID: mdl-39120238

RESUMEN

BACKGROUND: Oncology nurses support cancer patients in meeting their self-care needs, often neglecting their own emotions and self-care needs. This study aims to investigate the variations in the five facets of holistic mindfulness among Italian oncology nurses based on gender, work experience in oncology, and shift work. METHOD: A cross-sectional study was carried out in 2023 amongst all registered nurses who were employed in an oncology setting and working in Italy. RESULTS: There were no significant differences in all five facets of holistic mindfulness (p ≥ 0.05) according to gender, work experience in the oncology field, and shift work. CONCLUSION: Could holistic mindfulness be defined as an intrinsic individual characteristic? Surely, more insights will be necessary to better define the holistic trend in oncology nursing.

9.
Sci Rep ; 14(1): 14276, 2024 06 20.
Artículo en Inglés | MEDLINE | ID: mdl-38902523

RESUMEN

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.


Asunto(s)
Redes Neurales de la Computación , Neoplasias Orofaríngeas , Infecciones por Papillomavirus , Tomografía Computarizada por Rayos X , Humanos , Neoplasias Orofaríngeas/virología , Neoplasias Orofaríngeas/diagnóstico por imagen , Neoplasias Orofaríngeas/patología , Tomografía Computarizada por Rayos X/métodos , Infecciones por Papillomavirus/diagnóstico por imagen , Infecciones por Papillomavirus/virología , Infecciones por Papillomavirus/patología , Masculino , Femenino , Papillomaviridae , Persona de Mediana Edad , Anciano , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/virología , Carcinoma de Células Escamosas/patología , Carcinoma de Células Escamosas de Cabeza y Cuello/virología , Carcinoma de Células Escamosas de Cabeza y Cuello/diagnóstico por imagen , Carcinoma de Células Escamosas de Cabeza y Cuello/patología , Carga Tumoral , Virus del Papiloma Humano
10.
Comput Biol Med ; 172: 108132, 2024 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-38508058

RESUMEN

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.


Asunto(s)
Neoplasias de la Mama , Terapia Neoadyuvante , Humanos , Femenino , Terapia Neoadyuvante/métodos , Inteligencia Artificial , Medios de Contraste/uso terapéutico , Resultado del Tratamiento , Imagen por Resonancia Magnética/métodos , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/patología
11.
Cancer Med ; 12(22): 20663-20669, 2023 11.
Artículo en Inglés | MEDLINE | ID: mdl-37905688

RESUMEN

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.


Asunto(s)
Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/genética , Neoplasias de la Mama/metabolismo , Pronóstico , Terapia Neoadyuvante/métodos , Estudios Retrospectivos , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismo , Trastuzumab/uso terapéutico , Aprendizaje Automático , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico
12.
Sci Rep ; 13(1): 20605, 2023 11 23.
Artículo en Inglés | MEDLINE | ID: mdl-37996651

RESUMEN

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.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Aprendizaje Profundo , Neoplasias Pulmonares , Humanos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/cirugía , Neoplasias Pulmonares/diagnóstico por imagen , Recurrencia Local de Neoplasia/diagnóstico por imagen , Redes Neurales de la Computación
13.
Sci Rep ; 13(1): 8575, 2023 05 26.
Artículo en Inglés | MEDLINE | ID: mdl-37237020

RESUMEN

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.


Asunto(s)
Neoplasias de la Mama , Femenino , Humanos , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/genética , Terapia Combinada , Hormonas , Pronóstico , Modelos de Riesgos Proporcionales , Receptor ErbB-2/genética , Aprendizaje Automático
14.
PLoS One ; 18(5): e0285188, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37130116

RESUMEN

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.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/genética , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/genética , Tomografía Computarizada por Rayos X/métodos , Aprendizaje Automático
15.
Healthcare (Basel) ; 11(7)2023 Apr 05.
Artículo en Inglés | MEDLINE | ID: mdl-37046969

RESUMEN

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing.

16.
Front Oncol ; 13: 1181792, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37519818

RESUMEN

Introduction: It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO. Methods: In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO. Results: The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%. Discussion: In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.

17.
Front Med (Lausanne) ; 10: 1116354, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36817766

RESUMEN

Introduction: Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Methods: Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Results: Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Discussion: Thus, our framework aims at shortening the distance between AI and clinical practice.

18.
Cancers (Basel) ; 14(9)2022 Apr 28.
Artículo en Inglés | MEDLINE | ID: mdl-35565344

RESUMEN

Characterization of breast cancer into intrinsic molecular profiles has allowed women to live longer, undergoing personalized treatments. With the aim of investigating the relation between different values of ki67 and the predisposition to develop a breast cancer-related IDE at different ages, we enrolled 900 patients with a first diagnosis of invasive breast cancer, and we partitioned the dataset into two sub-samples with respect to an age value equal to 50 years. For each sample, we performed a Kaplan−Meier analysis to compare the IDE-free survival curves obtained with reference to different ki67 values. The analysis on patients under 50 years old resulted in a p-value < 0.001, highlighting how the behaviors of patients characterized by a ki67 ranging from 10% to 20% and greater than 20% were statistically significantly similar. Conversely, patients over 50 years old characterized by a ki67 ranging from 10% to 20% showed an IDE-free survival probability significantly greater than patients with a ki67 greater than 20%, with a p-value of 0.01. Our work shows that the adoption of two different ki67 values, namely, 10% and 20%, might be discriminant in designing personalized treatments for patients under 50 years old and over 50 years old, respectively.

19.
J Pers Med ; 12(6)2022 Jun 10.
Artículo en Inglés | MEDLINE | ID: mdl-35743737

RESUMEN

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori "Giovanni Paolo II" in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

20.
Artículo en Inglés | MEDLINE | ID: mdl-36612562

RESUMEN

Lean management is a relatively new organizational vision transferred from the automotive industry to the healthcare and administrative sector based on analyzing a production process to emphasize value and reduce waste. This approach is particularly interesting in a historical moment of cuts and scarcity of economic resources and could represent a low-cost organizational solution in many production companies. In this work, we analyzed the presentation and the initial management of current ministerial research projects up to the approval by the Scientific Directorate of an Italian research institute. Furthermore, the initial mode in 2021 ("as is") and the potential mode ("to be") according to a Lean model are studied, according to the current barriers highlighted by the final users of the process and carrying out some perspective analyses with some reference indicators.


Asunto(s)
Eficiencia Organizacional , Neoplasias , Industrias , Atención a la Salud , Academias e Institutos , Innovación Organizacional
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