Breast cancer in women younger than 40 years old: incidence, imaging features and radiation exposure.

SCOPO DELLA TESI: 1) Analisi retrospettiva dell’incidenza del carcinoma della mammella nella popolazione sotto i 40 anni; 2) Valutazione delle caratteristiche anatomo-patologiche e imaging delle lesioni maligne e benigne nella popolazione pre-screening negli ultimi 5 anni; 3) Monitoraggio della dose radiante nella popolazione in studio. MATERIALI E METODI: Revisione nei registri anatomo-patologici e sistema RIS-PACS della BREAST UNIT dell’Area Vasta Toscana Nordovest delle caratteristiche radiologiche, clinico-patologiche e biologiche delle lesioni maligne e benigne diagnosticate in donne di età < 40 anni nel periodo 01/01/2010- 31/12/2015. Le correlazioni lineari tra variabili sono espresse con indice di Pearson (pvalue <0.05 statisticamente significativo). Le variabili continue vengono espresse con valore medio e deviazione standard. Per la valutazione della dosimetria le informazioni in files dicom sono state elaborate tramite software creando un database in grado di analizzare la correlazione tra ogni singola acquisizione, lo spessore della mammella ed età della paziente. RISULTATI: L'incidenza del carcinoma della mammella nelle donne sotto i 40 anni è di 4.7:100000 abitanti nella popolazione generale e 9.1:100000 abitanti nella popolazione femminile. Stratificando per fasce d'età, negli ultimi 5 anni l'incidenza è stata di 2.7:100000 nelle donne con età tra 20-24 anni, 17:100000 in quelle tra 25-29, 32.5:100000 tra 30-34 e 105.1:100000 nelle donne tra i 35 e i 39 anni. Su 563 pazienti di età compresa tra 15-39 anni (età media 33), nell’arco di 5 anni 459 pazienti hanno avuto una diagnosi istologica di lesioni benigne (B2), 90 pazienti hanno avuto lesioni maligne (B5) (in media circa 15 cancri/anno), 13 pazienti hanno avuto lesioni borderline (B3) e una paziente una diagnosi di carcinoma lobulare in situ (B4). I carcinomi osservati negli stessi ospedali in donne di età>40aa sono complessivamente 420/anno. 84/90 (93%) donne hanno avuto carcinomi infiltranti: 46% erano Luminali B (31% Luminali B Her2Neu- e 15% Luminali B Her2Neu+), 14% Tripli negativi (TN), 12% Luminali A e 11% Her2Neu+ non Luminali (17% non disponibili per la revisione). Tutti i pazienti con diagnosi di carcinoma hanno fatto mammografia ed ecografia, solo 59/90 hanno fatto RM. All’eco il reperto più comune erano masse (88%): 61% con margini spiculati (BI-RADS 5), 21% microlobulati (BI-RADS 4) e solo 6% avevano margini regolari (BI-RADS 3). Alla mammografia più frequentemente si è riscontrato nel 49% dei casi masse associate o meno a calcificazioni: 38% con margini spiculati (BI-RADS 5), 9% con margini microlobulati (BI-RADS 4), 2% con margini circoscritti (BI-RADS3); nel 26% delle pazienti vi erano microcalcificazioni isolate (BIRADS 4). 46% pazienti erano asintomatiche e la lesione maligna è stata rilevata con ecografia di screening. Nel confronto benigno/maligno l’accuratezza diagnostica dell’ecografia per le lesioni benigne è stata 0.84 (IC 95%: 0.79-0.88) e per la diagnosi di malignità di 0.90 (IC 95%: 0.86-0.94) (P < 0.05). La dose radiante correla positivamente con lo spessore della mammella. CONCLUSIONI: Nelle donne < 40 anni il carcinoma della mammella è sporadico nella popolazione in studio e non si è evidenziato un trend di crescita negli ultimi 5 anni. Non è diagnosticato comunque solo in donne ad alto rischio. Nel nostro studio 65/90 (72%) cancri sono stati diagnosticati in donne non ad alto rischio e 50% di queste erano asintomatiche. L'ecografia può diagnosticare il 98% delle neoplasie mammarie ma bisogna considerare i costi di uno screening di popolazione per un evento sporadico. Nelle donne giovani la patologia infiltrante è più frequente di quella in situ (93% vs 7%), il pattern biologico di presentazione maggiormente osservato è stato il Luminale B e Her2Neu è risultato overespresso rispetto alle donne > 40 anni. Il pattern dominante di presentazione delle lesioni maligne è la massa a margini spiculati (nessuna differenza con il pattern delle donne > 40) e l'overespressione di Her2Neu si assocerebbe alla presenza di microcalcificazioni alla mammografia. All’US il pattern spiculato è stato osservato prevalentemente in cancri meno aggressivi (basso Ki-67), mentre il pattern microlobulato è più tipico di cancri con alto Ki-67. Le masse microlobulate possono essere sia maligne (Her2Neu Non Luminale e TN) che benigne, perciò la biopsia è raccomandata

TT virus levels in the plasma of infected individuals with different hepatic and extrahepatic pathology

TT virus (TTV) infection is extremely widespread in the general population. A sensitive real-time PCR assay was developed that quantitated accurately the most prevalent TTV genotypes in Italy. When used to test 217 individuals for TTV viraemia, the overall prevalence was 94%. Viraemia levels varied widely amongst individual subjects, with no major differences related to gender or age. The highest TTV titres were in haemophiliacs and in patients with non-A-E hepatitis, but they did not differ from the group with miscellaneous diseases. HIV- and HCV-infected subjects and patients with primary liver diseases had TTV loads similar to those of healthy individuals. (C) 2001 Wiley-Liss, Inc

Pathologic response and residual tumor cellularity after neo-adjuvant chemotherapy predict prognosis in breast cancer patients

Introduction: Residual tumor cellularity (RTC) and pathologic complete response (pCR) after neo-adjuvant chemotherapy (NAC) are prognostic factors associated with improved outcomes in breast cancer (BC). However, the majority of patients achieve partial pathologic response (pPR) and no clear correlation between RTC patterns and outcomes was described. Our aims were to define predictive factors for pCR and compare different outcomes of patients with pCR or pPR and with different RTC patterns. Materials and methods: Baseline and post-NAC demographics, clinicopathological characteristics, post-operative data, survival and recurrence status were recorded from our institutional database. A multivariable analysis was performed using a logistic regression model to identify independent predictors of pCR. Disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) analyses were performed using the Kaplan-Meier method. Results: Overall, of the 495 patients analyzed, 148 (29.9%) achieved pCR, 347 (70.1%) had pPR, and the median RTC was 40%. Multivariable analysis identified 3 independent factors predictive of pCR: tumor stage before NAC (cT1-2 84.5% versus cT3-4 15.5%), BC sub-type (HER2-positive 54.7% versus triple-negative 29.8% versus luminal-like 15.5%), and vascular invasion (absence 98.0% versus presence 2.0%). We found statistically significant longer DFS, DDFS, and OS in patients with pCR and with RTC <40%; no difference was observed in terms of OS between RTC <40% and RTC ≥40% groups. Conclusions: Tumor stage before NAC, BC sub-type, and vascular invasion are significant and independent factors associated with pCR. Patients with pCR and with RTC <40% have longer DFS, DDFS, and OS compared with patients with pPR

Dataset related to article "Pathologic response and residual tumor cellularity after neo-adjuvant chemotherapy predict prognosis in breast cancer patients"

&lt;p&gt;This record contains raw data related to article "Pathologic response and residual tumor cellularity after neo-adjuvant chemotherapy predict prognosis in breast cancer patients"&lt;/p&gt;&lt;p&gt;Abstract&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Residual tumor cellularity (RTC) and pathologic complete response (pCR) after neo-adjuvant chemotherapy (NAC) are prognostic factors associated with improved outcomes in breast cancer (BC). However, the majority of patients achieve partial pathologic response (pPR) and no clear correlation between RTC patterns and outcomes was described. Our aims were to define predictive factors for pCR and compare different outcomes of patients with pCR or pPR and with different RTC patterns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;Baseline and post-NAC demographics, clinicopathological characteristics, post-operative data, survival and recurrence status were recorded from our institutional database. A multivariable analysis was performed using a logistic regression model to identify independent predictors of pCR. Disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) analyses were performed using the Kaplan-Meier method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, of the 495 patients analyzed, 148 (29.9%) achieved pCR, 347 (70.1%) had pPR, and the median RTC was 40%. Multivariable analysis identified 3 independent factors predictive of pCR: tumor stage before NAC (cT1-2 84.5% versus cT3-4 15.5%), BC sub-type (HER2-positive 54.7% versus triple-negative 29.8% versus luminal-like 15.5%), and vascular invasion (absence 98.0% versus presence 2.0%). We found statistically significant longer DFS, DDFS, and OS in patients with pCR and with RTC &lt;40%; no difference was observed in terms of OS between RTC &lt;40% and RTC ≥40% groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Tumor stage before NAC, BC sub-type, and vascular invasion are significant and independent factors associated with pCR. Patients with pCR and with RTC &lt;40% have longer DFS, DDFS, and OS compared with patients with pPR.&lt;/p&gt

An approach to evaluate the quality of radiological reports in Head and Neck cancer loco-regional staging: experience of two Academic Hospitals

none24Objectives: To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (H&N) cancer. Methods: Consecutive reports of staging CT and MRI of all H&N cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories. Results: Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases. Conclusion: Reports of staging CT/MRI in H&N cancer were LQ for T description and HQ for N description.noneGiannitto, Caterina; Esposito, Andrea Alessandro; Spriano, Giuseppe; De Virgilio, Armando; Avola, Emanuele; Beltramini, Giada; Carrafiello, Gianpaolo; Casiraghi, Elena; Coppola, Alessandra; Cristofaro, Valentina; Farina, Davide; Gaino, Francesca; Lastella, Giulia; Lofino, Ludovica; Maroldi, Roberto; Piccoli, Francesca; Pignataro, Lorenzo; Preda, Lorenzo; Russo, Elena; Solimeno, Lorenzo; Vatteroni, Giulia; Vidiri, Antonello; Balzarini, Luca; Mercante, GiuseppeGiannitto, Caterina; Esposito, Andrea Alessandro; Spriano, Giuseppe; De Virgilio, Armando; Avola, Emanuele; Beltramini, Giada; Carrafiello, Gianpaolo; Casiraghi, Elena; Coppola, Alessandra; Cristofaro, Valentina; Farina, Davide; Gaino, Francesca; Lastella, Giulia; Lofino, Ludovica; Maroldi, Roberto; Piccoli, Francesca; Pignataro, Lorenzo; Preda, Lorenzo; Russo, Elena; Solimeno, Lorenzo; Vatteroni, Giulia; Vidiri, Antonello; Balzarini, Luca; Mercante, Giusepp

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively