Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy.

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.

Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach.

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.

Nine-Week Versus One-Year Trastuzumab for Early Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: 10-Year Update of the ShortHER Phase III Randomized Trial.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned coprimary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.We present the final analysis of the phase III noninferiority, randomized ShortHER trial comparing 9 weeks versus 1 year of adjuvant trastuzumab with chemotherapy in patients with human epidermal growth factor receptor 2-positive (HER2+) early breast cancer (BC). Women with HER2+ BC were randomly assigned to anthracycline-taxane combinations plus 1-year trastuzumab (arm A, long) or 9-week trastuzumab (arm B, short). Here, we report the second coprimary end point overall survival (OS), updated disease-free survival (DFS), and outcomes according to hormone receptor status, age, and nodal status. At a median follow-up of 9 years, 10-year DFS is 77% versus 78% in the long versus short arm, respectively. Ten-year OS is 89% versus 88% in the long versus short arm, respectively. 10-year DFS rates in the long versus short arm according to nodal status are N0 81% versus 85%; N1-3 77% versus 79%; and N4+ 63% versus 53%. Ten-year OS rates in long versus short arm according to nodal status are N0 89% versus 95%%; N1-3 92% versus 89%; and N4+ 84% versus 64%. The updated analysis of the ShortHER trial shows that 1-year trastuzumab is the standard treatment for patients with HER2+ early BC as noninferiority cannot be claimed. However, numerically, the differences for the patients at low or intermediate risk (N0/N1-3) is negligible, while patients with N4+ have a clear benefit with 1-year trastuzumab.

Adjuvant capecitabine in triple negative breast cancer patients with residual disease after neoadjuvant treatment: real-world evidence from CaRe, a multicentric, observational study.

Background: In triple negative breast cancer patients treated with neoadjuvant chemotherapy, residual disease at surgery is the most relevant unfavorable prognostic factor. Current guidelines consider the use of adjuvant capecitabine, based on the results of the randomized CREATE-X study, carried out in Asian patients and including a small subset of triple negative tumors. Thus far, evidence on Caucasian patients is limited, and no real-world data are available. Methods: We carried out a multicenter, observational study, involving 44 oncologic centres. Triple negative breast cancer patients with residual disease, treated with adjuvant capecitabine from January 2017 through June 2021, were recruited. We primarily focused on treatment tolerability, with toxicity being reported as potential cause of treatment discontinuation. Secondarily, we assessed effectiveness in the overall study population and in a subset having a minimum follow-up of 2 years. Results: Overall, 270 patients were retrospectively identified. The 50.4% of the patients had residual node positive disease, 7.8% and 81.9% had large or G3 residual tumor, respectively, and 80.4% a Ki-67 >20%. Toxicity-related treatment discontinuation was observed only in 10.4% of the patients. In the whole population, at a median follow-up of 15 months, 2-year disease-free survival was 62%, 2 and 3-year overall survival 84.0% and 76.2%, respectively. In 129 patients with a median follow-up of 25 months, 2-year disease-free survival was 43.4%, 2 and 3-year overall survival 78.0% and 70.8%, respectively. Six or more cycles of capecitabine were associated with more favourable outcomes compared with less than six cycles. Conclusion: The CaRe study shows an unexpectedly good tolerance of adjuvant capecitabine in a real-world setting, although effectiveness appears to be lower than that observed in the CREATE-X study. Methodological differences between the two studies impose significant limits to comparability concerning effectiveness, and strongly invite further research.

Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.

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.

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.

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.

Abemaciclib-associated Diarrhea: An Exploratory Analysis of Real-life Data.

BACKGROUND/AIM: Abemaciclib is a cyclin-dependent kinase 4/6 inhibitor approved in combination with endocrine therapy for treating hormone receptor-positive and human epidermal growth factor receptor 2-negative early and advanced breast cancer patients. The safety profile of abemaciclib is characterized by frequent gastrointestinal toxicity, especially diarrhea. Therefore, we performed an exploratory analysis of clinical factors that may be potentially associated with diarrhea in patients treated with abemaciclib plus endocrine therapy. PATIENTS AND METHODS: Factors potentially predisposing to diarrhea were selected, such as age ≥70 years, concomitant medications and diseases, diet, and use of laxatives. These variables were correlated with the onset of grade 2/3 diarrhea in a cohort of patients treated with abemaciclib from advanced breast cancer. Univariate and multivariate analysis was performed. Sensitivity and specificity were tested using the ROC curve. RESULTS: Eighty women with advanced breast cancer were included in the study. The univariate analysis found a statistically significant correlation between grade 2/3 diarrhea and age ≥70 years, polypharmacy, and concomitant gastrointestinal diseases (p<0.05). In the multivariate analysis, the number of risk factors significantly correlated with the outcome of interest (p<0.0001). ROC analysis showed our model's 82% sensitivity and 75% specificity. CONCLUSION: Taking into account specific pre-existing factors, it is possible to estimate the risk of diarrhea in hormone receptor-positive and human epidermal growth factor receptor 2-negative - advanced breast cancer patients, candidates for abemaciclib plus endocrine therapy. In these subjects, implementing proactive prevention and adopting a dose-escalation strategy may represent practical approaches to decrease the abemaciclib toxicity burden.

Clinical Outcomes of HER2-Negative Metastatic Breast Cancer Patients in Italy in the Last Decade: Results of the GIM 13-AMBRA Study.

GIM 13-AMBRA is a longitudinal cohort study aimed at describing therapeutic strategies and the relative outcome parameters in 939 HER2-ve MBC patients. Taxanes-based regimens, or taxanes + targeted agents, mainly Bevacizumab, were the preferred first choice in both Luminal (30.2%) and TNBC (33.3%) patients. The median PFS1 was 12.5 months (95% CI 16.79-19.64), without any significant difference according to subtypes, while the median Time to first Treatment Change (TTC1) was significantly lower in TNBC patients (7.7 months-95% CI 5.7-9.2) in comparison to Luminal A (13.2 months, 95% CI 11.7-15.1) and Luminal B patients (11.8 months, 95% CI 10.3-12.8). PFS2 was significantly shorter in TNBC patients (5.5 months, 95% CI 4.3-6.5 vs. Luminal A-9.4, 95% CI 8.1-10.7, and Luminal B-7.7 95% CI 6.8-8.2, F-Ratio 4.30, p = 0.014). TTC2 was significantly lower in patients with TNBC than in those with the other two subtypes. The median OS1 was 35.2 months (95% CI 30.8-37.4) for Luminal A patients, which was significantly higher than that for both Luminal B (28.9 months, 95% CI 26.2-31.2) and TNBC (18.5 months, 95% CI 16-20.1, F-ratio 7.44, p = 0.0006). The GIM 13-AMBRA study is one of the largest collections ever published in Italy and provides useful results in terms of time outcomes for first, second, and further lines of treatment in HER2- MBC patients.

HER2-Low Status Does Not Affect Survival Outcomes of Patients with Metastatic Breast Cancer (MBC) Undergoing First-Line Treatment with Endocrine Therapy plus Palbociclib: Results of a Multicenter, Retrospective Cohort Study.

BACKGROUND: Approximately 45-50% of breast cancers (BCs) have a HER2 immunohistochemical score of 1+ or 2+ with negative in situ hybridization, defining the "HER2-low BC" subtype. No anti-HER2 agents are currently approved for this subgroup in Europe, where treatment is still determined by HR expression status. In this study, we investigated the prognostic significance of HER2-low status in HR+/HER2- metastatic BC (MBC) patients treated with endocrine therapy (ET) plus palbociclib as first line. METHODS: We conducted a retrospective study including 252 consecutive HR+/HER2- MBC patients who received first-line ET plus palbociclib at six Italian Oncology Units between March 2016 and June 2021. The chi-square test was used to assess differences in the distribution of clinical and pathological variables between the HER-0 and HER2-low subgroups. Survival outcomes, progression-free survival (PFS) and overall survival (OS), were calculated by the Kaplan-Meier method, and the log-rank test was performed to estimate the differences between the curves. RESULTS: A total of 165 patients were included in the analysis: 94 (57%) and 71 (43%) patients had HER2-0 and HER2-low disease, respectively. The median age at treatment start was 64 years. No correlation between patients and tumor characteristics and HER2 status was found. Median PFS (mPFS) for the entire study cohort was 20 months (95% CI,18-25 months), while median OS (mOS) was not reached at the time of analysis. No statistically significant differences, in terms of PFS (p = 0.20) and OS (p = 0.1), were observed between HER2-low and HER2-0 subgroups. CONCLUSIONS: In our analysis, HR+ MBC patients with low HER2 expression who received first-line treatment with ET plus Palbociclib reported no statistically different survival outcomes compared to HER2-0 patients. Further prospective studies are needed to confirm the clinical role of HER2 expression level.

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.

Edoxaban: front-line treatment for brachiocephalic vein thrombosis in primitive mediastinal seminoma: A case report and literature review.

RATIONALE: Venous thromboembolism is a feared frequent complication of cancer with a 2-way relationship. Low molecular weight heparin is the mainstay of treatment. The use of direct oral anticoagulants is supported by established evidence for the treatment of deep vein thrombosis also in active cancer and they are prioritized over low molecular weight heparin for cancer-associated thrombosis according to current guidelines. However, upper limb deep vein thrombosis is poorly studied with scant data on the use of direct oral anticoagulants in noncatheter-related deep vein thrombosis. We report the case of a patient with noncatheter-related deep vein thrombosis and a rare tumor site effectively and safely treated with a direct oral anticoagulant, edoxaban, after lack of efficacy with low molecular weight heparin. PATIENT CONCERNS: A 35-year-old man with primitive mediastinal seminoma presented at our Cardio-Oncology Unit for prechemotherapy assessment. DIAGNOSIS: Persistent brachiocephalic deep vein thrombosis, despite full-dose enoxaparin, was detected at ultrasonography. INTERVENTION: We decided to switch the anticoagulant treatment from enoxaparin to edoxaban. OUTCOME: The 3-month ultrasonography showed almost total regression of the deep vein thrombosis without any adverse effects and a good patient compliance. LESSONS: We conducted a literature review on upper limb deep vein thrombosis, since its management is challenging due to inconsistency of evidence. This report highlights the benefits of direct oral anticoagulants compared to low molecular weight heparins in cancer-associated thrombosis therapy in terms of efficacy, safety and ease of use.

Cancer-Associated Thrombosis: Not All Low-Molecular-Weight Heparins Are the Same, Focus on Tinzaparin, A Narrative Review.

Cancer-associated thrombosis (CAT) is the second main cause of cancer death with high related mortality and morbidity, leading to anticancer agent delays and interruptions. The recommended therapy, low-molecular-weight heparin (LMWH), however, is burdensome for patients and costly for society, as treatment should last until cancer is no longer active, even indefinitely. Tinzaparin is a manageable, efficient, safe, and cost-effective option. Compared to the other LMWHs, advantages are single-daily dose and safety in the elderly and those with renal impairment (RI). The purpose of this review is to critically discuss recent data on its efficacy and safety in CAT.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

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.

Pathological Complete Response to Neoadjuvant Chemoimmunotherapy for Early Triple-Negative Breast Cancer: An Updated Meta-Analysis.

Immune checkpoint inhibitors (ICIs) have made a breakthrough in the systemic treatment for metastatic triple-negative breast cancer (TNBC) patients. However, results of phase II and III clinical trials assessing ICIs plus chemotherapy as neoadjuvant treatment were controversial and conflicting. We performed a meta-analysis aimed at assessing the Odds Ratio (OR) of the pathological complete response (pCR) rate in trials assessing neoadjuvant chemoimmunotherapy in TNBC. According to our results, the use of neoadjuvant chemoimmunotherapy was associated with higher pCR (OR 1.95; 95% Confidence Intervals, 1.27-2.99). In addition, we highlighted that this benefit was observed regardless of PD-L1 status since the analysis reported a statistically significant and clinically meaningful benefit in both PD-L1 positive and PD-L1 negative patients. These findings further support the exploration of the role of ICIs plus chemotherapy in early-stage TNBC, given the potentially meaningful clinical impact of these agents. Further studies aimed at more deeply investigating this emerging topic in breast cancer immunotherapy are warranted.

An Invasive Disease Event-Free Survival Analysis to Investigate Ki67 Role with Respect to Breast Cancer Patients' Age: A Retrospective Cohort Study.

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.

A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients.

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 soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.

Adenosine pathway inhibitors: novel investigational agents for the treatment of metastatic breast cancer.

INTRODUCTION: The adenosine pathway has been suggested to play a key role in several carcinogenetic processes, with the metabolism of adenosine-5'-triphosphate (ATP) and its derivatives reported to be dysregulated in breast cancer. Preclinical evidence has supported the role of adenosine in the pathogenesis of this malignancy as well as the development of selective adenosine pathway inhibitors. AREAS COVERED: The paper overviews the evidence regarding the use of adenosine pathway inhibitors in breast cancer; a literature search was conducted in January 2022 of Pubmed/Medline, Cochrane library, and Scopus databases. EXPERT OPINION: The adenosine pathway regulates inflammation, apoptosis, metastasis, and cell proliferation in breast cancer cells, and adenosine pathway inhibitors have yielded encouraging results in early-phase clinical trials. Well-designed, multicenter studies focused on monotherapies and combination therapies (which include immune checkpoint inhibitors) are warranted in this setting.

[A strange case of wide QRS tachycardia during paclitaxel chemotherapy]. / Uno strano caso di tachicardia a QRS largo in corso di chemioterapia con paclitaxel.

We report the case of a 50-year-old female patient with breast cancer who, during preoperative workup, presented repeated wide QRS complex tachycardias, recorded by Holter ECG. She was immediately referred to a hub center for electrophysiological study where she was diagnosed with right ventricular outflow tract ventricular tachycardia and underwent catheter ablation. The chemotherapy with paclitaxel that the patient was receiving combined with psychological stress may have triggered the arrhythmias.

Prognostic Value of NLRP3 Inflammasome and TLR4 Expression in Breast Cancer Patients.

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 = 0.009, respectively). In multivariate analysis, TLR4 was confirmed as independent prognostic factors for DFS (HR = 2.03, 95% CI 1.16-3.57, p = 0.014), and high NLRP3 expression showed a slight association with DFS (HR = 1.75, 95% CI 0.98-3.15, p = 0.06). In conclusion, we showed TLR4 expression as independent prognostic factors and we highlighted for the first time that high expression of NLRP3 is linked to a poor prognosis in BC patients. These results suggest that NLRP3 and TLR4 could be two new good prognostic factor for BC patients.

Psychological well-being in cancer outpatients during COVID-19.

PURPOSE: The psychological status of cancer outpatients receiving anti-neoplastic treatment during the lockdown in a Italian non-COVID Cancer Center, was been investigated with the following aims: to measure the levels of post-traumatic stress symptoms, depression and anxiety; to compare patients with different cancer sites; to compare the anxiety and depression levels measured in this emergency period between cancer and non-cancer patients and between cancer patients before and after the emergency. METHODS: The following questionnaires were used: The Hospital Anxiety and Depression Scale (HADs) and the Impact of Event Scale-Revised (IES-R).Worries regarding the COVID-19 on patients' lives, socio-demographic and clinical details were collected using a brief structured questionnaire. RESULTS: One-hundred seventy-eight outpatients were enrolled. We found that 55% of patients were above the cut-off for HADS general scale and 23.7% had severe level of PTSD. The 68% of patients declared that their worries have increased during the pandemic especially for women. Patients with lung cancer have higher general distress compared with patients with breast cancer and lymphoma. The non cancer sample had values significantly higher both for the IES-R scales and for HADS Depression subscale. Finally, cancer patients who experienced the health emergency showed higher levels of anxiety than those measured 2 years ago. CONCLUSION: Cancer out-patients of the present sample have severe post-traumatic stress symptoms and psychological distress, those with lung cancer are at higher risk and may need special attention. Non-oncological subjects have higher depression levels than cancer patients.