Nipple-Areola Complex Reconstruction Using FixNip NRI Implant after Mastectomy: An Innovative Technique

BACKGROUND

Nipple-areolar complex reconstruction is the final stage of breast reconstruction, and it improves quality of life in patients with post-mastectomy breast cancer. We present a case of a patient with breast cancer underwent breast reconstruction and subsequent nipple-areolar complex reconstruction with an innovative biocompatible smooth silicone implant specially designed for a long-lasting restoration of the nipple-areola complex called FixNip NRI. However, to our knowledge, nipple-areolar complex reconstruction with FixNip was not previously reported.

INNOVATIVE TECHNIQUE

We present an emerging technique applied on a patient with breast cancer treated with skin-sparing mastectomy and with immediate breast reconstruction using an expander and then exchanged expander to breast implant. FixNip nipple reconstruction implant is implanted for the reconstruction of the areola-nipple complex with local-regional anaesthesia. She did not develop any postoperatively short-term or long-term complications, and her nipple slowly underwent to a gradual and better definition of its profile.

CONCLUSION

This new approach regarding the reconstruction of the nipple-areola complex seems to be very promising in relation to both the degree of aesthetic satisfaction of patients and the ease of use by surgeons.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma

Introduction

Malignant pleural mesothelioma (MPM) is a poor-prognosis disease. Owing to the recent availability of new therapeutic options, there is a need to better assess prognosis. The initial clinical response could represent a useful parameter.

Methods

We proposed a transfer learning approach to predict an initial treatment response starting from baseline CT scans of patients with advanced/unresectable MPM undergoing first-line systemic therapy. The therapeutic response has been assessed according to the mRECIST criteria by CT scan at baseline and after two to three treatment cycles. We used three slices of baseline CT scan as input to the pre-trained convolutional neural network as a radiomic feature extractor. We identified a feature subset through a double feature selection procedure to train a binary SVM classifier to discriminate responders (partial response) from non-responders (stable or disease progression).

Results

The performance of the prediction classifiers was evaluated with an 80:20 hold-out validation scheme. We have evaluated how the developed model was robust to variations in the slices selected by the radiologist. In our dataset, 25 patients showed an initial partial response, whereas 13 patients showed progressive or stable disease. On the independent test, the proposed model achieved a median AUC and accuracy of 86.67% and 87.50%, respectively.

Conclusions

The proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness.

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

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.

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study

BACKGROUND

To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task.

METHODS

The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relating to 535 patients suffering from lung cancer. Seven algorithms were trained independently by participants in the AI4MP working group on Artificial Intelligence of the Italian Association of Physics in Medicine to discriminate metastatic from non-metastatic patients. The classification scores generated by these algorithms are used to train SVM classifier. The Explainable Artificial Intelligence approach is applied to the final model. The ensemble model was validated following an 80-20 hold-out and leave-one-out scheme on the training set.

RESULTS

Compared to individual algorithms, a more accurate result was achieved. On the independent test the ensemble model achieved an accuracy of 0.78, a F1-score of 0.57 and a log-loss of 0.49. Shapley values representing the contribution of each algorithm to the final classification result of the ensemble model were calculated. This information represents an added value for the end user useful for evaluating the appropriateness of the classification result on a particular case. It also allows us to evaluate on a global level which methodological approaches of the individual algorithms are likely to have the most impact.

CONCLUSION

Our proposal represents an innovative approach useful for integrating different algorithms that populate the literature and which lays the foundations for future evaluations in broader application scenarios.

Generative Artificial Intelligence (AI) to Uncover Insights From Breast Cancer Patients' Perceptions to Mindfulness-Based Stress Reduction (MBSR) Interventions

The study's central objective is to harness the power of generative Artificial Intelligence (AI), in particular based on Large Language Models, as a valuable resource for delving deeper into the insights offered by patients with breast cancer (BC) who actively participated in a Mindfulness-Based Stress Reduction (MBSR) program. In a 6-week MBSR program, each session lasted 2 hours and encompassed a range of techniques, including sitting meditation, body scan, Hatha yoga, and walking meditation. A total of 25 participants were enrolled in the study. The majority of these participants reported a high level of satisfaction with the mindfulness course. The application of generative AI enabled a comprehensive analysis of the participants' responses, revealing distinct subgroups among them. The MBSR program was found to be beneficial for most participants, serving as a valuable tool in managing the psychological stresses associated with BC.

Variations in the Five Facets of Mindfulness in Italian Oncology Nurses according to Sex, Work Experience in Oncology, and Shift Work

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.

Care Nursing in Immune Disorder Assessment among Adult Oncology Patients: A Scoping Review

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.

Nursing Coaching Can Improve the Quality of Life and Immune-Endocrine Condition in Hospitalized Cancer Patients

INTRODUCTION

Any cancer diagnosis induces fear and shocking emotional experiences accompanied by anxiety, depression, unpredictability, and distress. The emotional effect of a cancer diagnosis and the rigidity of cancer treatment negatively impact the quality of life (QoL) of patients, and this may continue after treatment. Additionally, emotional distress induces neuroendocrine stress activation systems and raises stress hormone secretion by causing immunological dysfunctions. The present narrative review aims to describe nursing coaching approaches that improve QoL perceptions among cancer patients during their hospitalization.

METHODS

This review was carried out using the PRISMA methodology until the end of November 2023 through PubMed, Scopus, Web of Science, and CINAHL databases. Researchers systematically collected all the currently available literature. The search terms and boolean operators used to combine keywords were: "QoL" AND "hospitalization" AND "cancer patients" AND "nursing coaching".

RESULTS

Four manuscripts were selected in the present review. One manuscript belonged to the British Nursing Database and was a mixed-block-randomized study; one belonged to Scopus, which was also in the PubMed, WoS, and Medline and was a study protocol for an RCT and two manuscripts belonged to the PubMed database and were all RCTs.

CONCLUSION

Nursing coaching improved QoL perceptions in cancer patients during their hospitalization. Patients were found to prefer in-person interventions to nurse-led ones, which improved QoL perceptions. However, further interventional studies need to be performed in order to better address coaching nursing interventions during the hospitalization of cancer patients.

The role of immune checkpoint inhibitors in the first-line treatment for patients with advanced biliary tract cancer: a systematic review and meta-analysis of randomized trials

Background

We performed a systematic review and meta-analysis to further explore the impact of the addition of immunotherapy to gemcitabine-cisplatin as first-line treatment for advanced biliary tract cancer (BTC) patients.

Methods

Literature research was performed, and hazard ratio values and 95% confidence intervals were calculated. Heterogeneity among studies was assessed using the tau-squared estimator ( τ 2 ) . The total Cochrane Q test (Q) was also assessed. The overall survival rate, objective response rate, and progression-free survival in the selected studies were assessed.

Results

A total of 1,754 participants were included. Heterogeneity among the studies selected was found to be non-significant (p = 0.78; tau2 = 0, I2 = 0%). The model estimation results and the forest plot suggested that the test for the overall effect was significant (Z = -3.51; p< 0.01).

Conclusion

The results of the current meta-analysis further confirm the role of immune checkpoint inhibitors plus gemcitabine-cisplatin as the new standard first-line treatment for advanced BTC patients.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42023488095.

Attitudes, Knowledge and Clinical Practice of Health Professionals towards Psychological Disorders in Cancer Patients: An Observational Study

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.

Breast Cancer Prevention Practices and Knowledge in Italian and Chinese Women in Italy: Clinical Checkups, Free NHS Screening Adherence, and Breast Self-Examination (BSE)

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.

Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images

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.

Sacituzumab govitecan vs. chemotherapy for metastatic breast cancer: a meta-analysis on safety outcomes

Aim: There is limited data available regarding the comparison of Sacituzumab govitecan (SG) vs. chemotherapy in metastatic breast cancer patients.Materials & methods: We performed a systematic review and meta-analysis aimed to assess the safety profile of SG vs. chemotherapy for metastatic breast cancer (mBC) clinical trials.Results: The pooled odds ratio for outcomes such as grade 3-4 and all grade neutropenia, leukopenia, anemia and other non-hematological adverse events showed a higher risk for patients receiving SG. No statistically significant differences were reported in terms of grade 3-4 fatigue, all grade nausea, febrile neutropenia and treatment discontinuation due to adverse events.Conclusion: Our data, coupled with a statistically and clinically meaningful survival benefit, support the use of SG for mBC.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

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.

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

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.

Sex Differences in Anxiety and Depression Conditions among Cancer Patients: A Systematic Review and Meta-Analysis

(1) Background: Evidence suggested inconsistent results in anxiety and depression scores among female and male cancer patients. The present systematic review and meta-analysis aimed to assess how anxiety and depression conditions among cancer patients vary according to sex. (2) Methods: This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). The protocol was registered in PROSPERO with id no. CRD42024512553. The search strategy involved combining keywords using Boolean operators, including "Anxiety", "Cancer", and "Depression", across several databases: Embase, PubMed, Scopus, and Web of Science. The outcomes were evaluated using the Hospital Anxiety and Depression Scale (HADS). (3) Results: Data were collected from five studies, enrolling a total of 6317 cancer patients, of whom 2961 were females and 3356 males. For each study, HADS-A and HADS-D scores were considered, also differentiating HADS scores according to cancer typology, and then three different meta-analyses were performed. Generally, females reported significantly higher levels of depression scores than males and, conversely, males reported significantly greater levels of anxiety than females. (4) Conclusions: Previous studies suggested higher rates of depression and anxiety conditions in females than in males, but the present data highlighted controversial findings, since males reported significantly higher levels of anxiety than females. In this scenario, the theoretical approach justified females being more open than males to expressing anxiety or depression conditions. It would be necessary for healthcare professionals to improve effective measures purposed at assessing and mitigating depressive symptoms in cases of advanced cancer, thereby improving their mental health, given the high rates of depression in advanced cancer patients, due to the difficulty level of performing their daily living activities, which deteriorate further over time.

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.

Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis

Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.

The impact of ECOG performance status on efficacy of immunotherapy and immune-based combinations in cancer patients: the MOUSEION-06 study

ECOG performance status (PS) is a pivotal prognostic factor in a wide number of solid tumors. We performed a meta-analysis to assess the role of ECOG PS in terms of survival in patients with ECOG PS 0 or ECOG PS 1 treated with immunotherapy alone or combined with other anticancer treatments. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, all phase II and III randomized clinical trials that compared immunotherapy or immune-based combinations in patients with solid tumors were retrieved. The outcomes of interest were overall survival (OS) and progression-free survival (PFS). We also performed subgroup analyses focused on type of therapy (ICI monotherapy or combinations), primary tumor type, setting (first line of treatment, subsequent lines). Overall, 60 studies were included in the analysis for a total of 35.020 patients. The pooled results showed that immunotherapy, either alone or in combination, reduces the risk of death or progression in both ECOG PS 0 and 1 populations. The survival benefit was consistent in all subgroups. Immune checkpoint inhibitors monotherapy or immune-based combinations are associated with improved survival irrespective of ECOG PS 0 or 1. Clinical trials should include more frail patients to assess the value of immunotherapy in these patients.

Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence

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.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

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.

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.

Comparison of multiple definitions for ventilator-associated pneumonia in patients requiring mechanical ventilation for non-pulmonary conditions: preliminary data from PULMIVAP, an Italian multi-centre cohort study

OBJECTIVES

To compare intensivist-diagnosed ventilator-associated pneumonia (iVAP) with four established definitions, assessing their agreement in detecting new episodes.

METHODS

A multi-centric prospective study on pulmonary microbiota was carried out in patients requiring mechanical ventilation (MV). Data collected were used to compare hypothetical VAP onset according to iVAP with the study consensus criteria, the European Centre for Disease Control and Prevention definition, and two versions of the latter adjusted for leukocyte count and fever.

RESULTS

In our cohort of 186 adult patients, iVAPs were 36.6% (68/186, 95% confidence interval 30.0-44.0%), with an incidence rate of 4.64/100 patient-MV-days, and median MV-day at diagnosis of 6. Forty-seven percent of patients (87/186) were identified as VAP by at least one criterion, with a median MV-day at diagnosis of 5. Agreement between intensivist judgement (iVAP/no-iVAP) and the criteria was highest for the study consensus criteria (50/87, 57.4%), but still one-third of iVAP were not identified and 9% of patients were identified as VAP contrary to intensivist diagnosis. VAP proportion differed between criteria (25.2-30.1%).

CONCLUSIONS

Caution is needed when evaluating studies describing VAP incidence. Pre-agreed criteria and definitions that capture VAP's evolving nature provide greater consistency, but new clinically driven definitions are needed to align surveillance and diagnostic criteria with clinical practice.

An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy

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.

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.