IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated ... more IntroductionRecently, 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.MethodsThus, 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.ResultsAge, 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.Disc...
For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of add... more For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is controversial. 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 approxi...
To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magne... more 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...
In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is ... more In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a ...
BACKGROUND: Breast cancer (BC) is a heterogeneous disease, and patients with apparently similar c... more BACKGROUND: Breast cancer (BC) is a heterogeneous disease, and patients with apparently similar clinicopathological characteristics in clinical practice show different outcome. This study evaluated in primary BCs and in the subgroup of the triple-negative breast cancers (TNBCs) the level of tumor infiltrating lymphocytes (TILs), Na þ /H þ exchanger regulatory factor 1 (NHERF1) expression, and their association respect to the clinical outcome of patients. MATERIAL AND METHODS: NHERF1 expression was assessed by immunohistochemistry in 338 BC samples; the analysis of TILs was examined using hematoxylin and eosin stained slides, according to International TILs Working Group 2014. RESULTS: Multivariate analysis identified TILs as an independent prognostic factor for DFS in the entire cohort and in the TNBC subgroup (HR, 0.32; 95% CI, 0.12e0.87; P ¼ 0.026; and HR, 0.22; 95% CI, 0.06e0.80; P ¼ 0.022, respectively). Univariate and survival analysis by KaplaneMeier method revealed that patients with cytoplasmic (c) NHERF1-/TILs þ expression had better DFS than other patients (P ¼ 0.049), and this result was also found in the TNBC subgroup (P ¼ 0.031). Moreover, TNBC patients with cNHERF1 À /TILs À expression had a worse DFS and OS than other patients (P ¼ 0.057 and P ¼ 0.002, respectively). CONCLUSIONS: In the complex scenario of BC and in the era of tumor immunogenicity and immunotherapy, we found an association of TIL levels and cNHERF1 expression that could be useful to identify BCs and particularly TNBC patients with different prognosis and clinical outcome.
Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment tha... more Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment that plays the central role in new anticancer drugs. TILs has a strong prognostic role in triple negative breast cancer (TNBC). Little is known about his interaction with Androgen receptor (AR) and Forkhead box A1 (FOXA1). We analyzed the relationships between TIL levels, AR and FOXA1 expression and their clinical significance in TNBC patients. Further, we investigated their interaction with other biomarkers like programmed cell death ligand-1 (PD-L1), Breast Cancer Type 1 susceptibility protein (BRCA1), Poly [ADP-Ribose] Polymerase 1 (PARP1) and Na+/H+ Exchanger Regulatory Factor 1 (NHERF1). The expression of the proteins was evaluated by immunohistochemistry in 124 TNBC samples. TILs were performed adhering to International TILs Working Group 2014 criteria. Cox proportional hazards models were also used to identify risk factors associated with poor prognosis. Multivariate analysis identif...
Journal of experimental & clinical cancer research : CR, Jan 2, 2018
Tumor microenvironment (TME) includes many factors such as tumor associated inflammatory cells, v... more Tumor microenvironment (TME) includes many factors such as tumor associated inflammatory cells, vessels, and lymphocytes, as well as different signaling molecules and extracellular matrix components. These aspects can be de-regulated and consequently lead to a worsening of cancer progression. In recent years an association between the scaffolding protein Na/H exchanger regulatory factor 1 (NHERF1) and tumor microenvironment changes in breast cancer (BC) has been reported. Subcellular NHERF1 localization, vascular endothelial growth factor (VEGF), its receptor VEGFR1, hypoxia inducible factor 1 alpha (HIF-1α), TWIST1 expression and microvessel density (MVD) in 183 invasive BCs were evaluated, using immunohistochemistry on tissue microarrays (TMA). Immunofluorescence was employed to explore protein interactions. Cytoplasmic NHERF1(cNHERF1) expression was directly related to cytoplasmic VEGF and VEGFR1 expression (p = 0.001 and p = 0.027 respectively), and inversely to nuclear HIF-1α (...
The role of rapid on site evaluation on touch imprint cytology and brushing during conventional bronchoscopy
Diagnostic Cytopathology, 2021
The increase in immunohistochemical and molecular predictive tests in lung cancer requires new st... more The increase in immunohistochemical and molecular predictive tests in lung cancer requires new strategies for managing small samples taken during bronchoscopic procedures. The value of Rapid On Site Evaluation (ROSE) during conventional bronchoscopic procedures on endobronchial neoplasms in optimizing small biopsies and cytologlogical tissue specimens for diagnostic testing, and ancillary studies was evaluated.
The mortality associated to breast cancer is in many cases related to metastasization and recurre... more The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years ...
Learning tasks are implemented via mappings of the sampled data set, including both the classical... more Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the perform...
The current study examined if cancer biomarker phenotyping could predict the clinical/pathologica... more The current study examined if cancer biomarker phenotyping could predict the clinical/pathological status of axillary nodes in women with primary breast cancer. Primary breast cancers from 2002 were analyzed for tumor size, estrogen receptor (ER), progesterone receptor (PgR), Ki-67MIB expression and Her2/neu amplification. Relationships between the clinical and pathological status of the axilla and the biological subtypes classification were analyzed using univariate, multivariate and regression tree analysis. A total of 65% of women with axillary nodes clinically involved had complete axillary node dissection (ALND) while 705 women with clinically negative axillary underwent sentinel lymph node biopsy (SLNB), 18.5% of the latter had at least one pathologically SLNB involved node. Multivariate analysis revealed that the Luminal A subtype was significantly associated (OR 0.62; P<10-9) with clinical negative axilla while HER2pos/not Luminal was associated with clinical positivity (OR 1.71; P<0.01). No significant association between biological subtypes and SLNB status was demonstrated. Regression tree analysis revealed that subgroups with significantly different probability of SLNB status were separated according to tumor size and PgR values. In conclusion, the current study demonstrated that biomarker breast cancer phenotyping is significantly associated with clinical status of axillary nodes but not with pathological involvement of nodes at SLNB. Regression tree analysis could represent a valid attempt to individualize some patients subgroups candidate to different surgical axilla approaches.
In the absence of lymph node abnormalities detectable on clinical examination or imaging, the gui... more In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our datas...
Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with c... more Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 w...
Prognostic Value of NLRP3 Inflammasome and TLR4 Expression in Breast Cancer Patients
Frontiers in Oncology
Inflammasome complexes play a pivotal role in different cancer types. NOD-like receptor protein 3... more Inflammasome complexes play a pivotal role in different cancer types. NOD-like receptor protein 3 (NLRP3) inflammasome is one of the most well-studied inflammasomes. Activation of the NLRP3 inflammasome induces abnormal secretion of soluble cytokines, generating advantageous inflammatory surroundings that support tumor growth. The expression levels of the NLRP3, PYCARD and TLR4 were determined by immunohistochemistry in a cohort of primary invasive breast carcinomas (BCs). We observed different NLRP3 and PYCARD expressions in non-tumor vs tumor areas (p<0.0001). All the proteins were associated to more aggressive clinicopathological characteristics (tumor size, grade, tumor proliferative activity etc.). Univariate analyses were carried out and related Kaplan-Meier curves plotted for NLRP3, PYCARD and TLR4 expression. Patients with higher NLRP3 and TLR4 expression had worse 5-year disease-free survival (DFS) compared to patients with lower NLRP3 and TLR4 expression (p =0.021 and p...
Should Tumor Infiltrating Lymphocytes, Androgen Receptor, and FOXA1 Expression Predict the Clinical Outcome in Triple Negative Breast Cancer Patients?
Cancers
Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment tha... more Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment that plays the central role in new anticancer drugs. TILs have a strong prognostic role in triple negative breast cancer (TNBC). Little is known about the interaction with the androgen receptor (AR) and forkhead box A1 (FOXA1). We analyzed the relationships between TIL levels, AR, and FOXA1 expression and their clinical significance in TNBC patients. Further, we investigated their interaction with other biomarkers like programmed cell death ligand-1 (PD-L1), breast cancer type 1 susceptibility protein (BRCA1), poly (ADP-Ribose) polymerase 1 (PARP1), and Na+/H+ exchanger regulatory factor 1 (NHERF1). The expression of the proteins was evaluated by immunohistochemistry in 124 TNBC samples. TILs were performed adhering to International TILs Working Group 2014 criteria. Cox proportional hazards models were also used to identify risk factors associated with poor prognosis. Multivariate analysis ...
Should Tumor Infiltrating Lymphocytes, Androgen Receptor, and FOXA1 Expression Predict the Clinical Outcome in Triple Negative Breast Cancer Patients?
Cancers
Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment tha... more Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment that plays the central role in new anticancer drugs. TILs have a strong prognostic role in triple negative breast cancer (TNBC). Little is known about the interaction with the androgen receptor (AR) and forkhead box A1 (FOXA1). We analyzed the relationships between TIL levels, AR, and FOXA1 expression and their clinical significance in TNBC patients. Further, we investigated their interaction with other biomarkers like programmed cell death ligand-1 (PD-L1), breast cancer type 1 susceptibility protein (BRCA1), poly (ADP-Ribose) polymerase 1 (PARP1), and Na+/H+ exchanger regulatory factor 1 (NHERF1). The expression of the proteins was evaluated by immunohistochemistry in 124 TNBC samples. TILs were performed adhering to International TILs Working Group 2014 criteria. Cox proportional hazards models were also used to identify risk factors associated with poor prognosis. Multivariate analysis ...
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