1
|
Petrillo A, Fusco R, Petrosino T, Vallone P, Granata V, Rubulotta MR, Pariante P, Raiano N, Scognamiglio G, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Sorgente E, Pecori B, Cerciello V, Boldrini L. A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer. Radiol Med 2024:10.1007/s11547-024-01817-8. [PMID: 38755477 DOI: 10.1007/s11547-024-01817-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024]
Abstract
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.
Collapse
Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Teresa Petrosino
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Paolo Vallone
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Maria Rosaria Rubulotta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Paolo Pariante
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Nicola Raiano
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Giosuè Scognamiglio
- Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale Di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale Di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Eugenio Sorgente
- Radiation Protection and Innovative Technology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Biagio Pecori
- Radiation Protection and Innovative Technology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenzo Cerciello
- Medical Physics, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| |
Collapse
|
2
|
Comes MC, Fanizzi A, Bove S, Didonna V, Diotiaiuti S, Fadda F, La Forgia D, Giotta F, Latorre A, Nardone A, Palmiotti G, Ressa CM, Rinaldi L, Rizzo A, Talienti T, Tamborra P, Zito A, Lorusso V, Massafra R. Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy. Comput Biol Med 2024; 172:108132. [PMID: 38508058 DOI: 10.1016/j.compbiomed.2024.108132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 03/22/2024]
Abstract
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.
Collapse
Affiliation(s)
- Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Vittorio Didonna
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Sergio Diotiaiuti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Federico Fadda
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Francesco Giotta
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Agnese Latorre
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annalisa Nardone
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Gennaro Palmiotti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Cosmo Maurizio Ressa
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Lucia Rinaldi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Tiziana Talienti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
3
|
Gatta G, Somma F, Sardu C, De Chiara M, Massafra R, Fanizzi A, La Forgia D, Cuccurullo V, Iovino F, Clemente A, Marfella R, Grezia GD. Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis. J Pers Med 2023; 13:1683. [PMID: 38138910 PMCID: PMC10744838 DOI: 10.3390/jpm13121683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Gianluca Gatta
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Somma
- U.O.C. Neurodiology, ASL Center Naples 1, P.O. Ospedale del Mare, 80147 Naples, Italy;
| | - Celestino Sardu
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
| | - Marco De Chiara
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaella Massafra
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Annarita Fanizzi
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Iovino
- Department of Translational Medical Science, School of Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Alfredo Clemente
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaele Marfella
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
| | | |
Collapse
|
4
|
Mollica V, Rizzo A, Marchetti A, Tateo V, Tassinari E, Rosellini M, Massafra R, Santoni M, Massari F. The impact of ECOG performance status on efficacy of immunotherapy and immune-based combinations in cancer patients: the MOUSEION-06 study. Clin Exp Med 2023; 23:5039-5049. [PMID: 37535194 DOI: 10.1007/s10238-023-01159-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
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.
Collapse
Affiliation(s)
- Veronica Mollica
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | | | - Andrea Marchetti
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Valentina Tateo
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Elisa Tassinari
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Matteo Rosellini
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | | | | | - Francesco Massari
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| |
Collapse
|
5
|
Fanizzi A, Fadda F, Comes MC, Bove S, Catino A, Di Benedetto E, Milella A, Montrone M, Nardone A, Soranno C, Rizzo A, Guven DC, Galetta D, Massafra R. Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence. Sci Rep 2023; 13:20605. [PMID: 37996651 PMCID: PMC10667245 DOI: 10.1038/s41598-023-48004-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/21/2023] [Indexed: 11/25/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Annarita Fanizzi
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Federico Fadda
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Samantha Bove
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Annamaria Catino
- Unità Operativa Complessa di Oncologia Toracica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Erika Di Benedetto
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Angelo Milella
- Dipartimento di ElettronicaInformazione e Bioingegneria, Politecnico di Milano, Via Giuseppe Ponzio, 34, 20133, Milan, Italy
| | - Michele Montrone
- Unità Operativa Complessa di Oncologia Toracica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annalisa Nardone
- Unità Operativa Complessa di Radioterapia, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Clara Soranno
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- Unità Operativa Complessa di Oncologia Medica 'Don Tonino Bello', I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Deniz Can Guven
- Department of Medical Oncology, Hacettepe University Cancer Institute, 06100, Sihhiye, Ankara, Turkey
| | - Domenico Galetta
- Unità Operativa Complessa di Oncologia Toracica, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo II', Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
6
|
Petrillo A, Fusco R, Barretta ML, Granata V, Mattace Raso M, Porto A, Sorgente E, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Trombadori CML, Belli P, Trecate G, Tenconi C, De Santis MC, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Boldrini L. Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome. Radiol Med 2023; 128:1347-1371. [PMID: 37801198 DOI: 10.1007/s11547-023-01718-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 10/07/2023]
Abstract
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.
Collapse
Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Maria Luisa Barretta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Mauro Mattace Raso
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annamaria Porto
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Eugenio Sorgente
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica-IRCCS, Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Paolo Belli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Giovanna Trecate
- Department of Radiodiagnostic and Magnetic Resonance, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Maria Carmen De Santis
- De Santis Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| |
Collapse
|
7
|
Fanizzi A, Latorre A, Bavaro DA, Bove S, Comes MC, Di Benedetto EF, Fadda F, La Forgia D, Giotta F, Palmiotti G, Petruzzellis N, Rinaldi L, Rizzo A, Lorusso V, Massafra R. Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach. Cancer Med 2023; 12:20663-20669. [PMID: 37905688 PMCID: PMC10709715 DOI: 10.1002/cam4.6512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 07/27/2023] [Accepted: 08/29/2023] [Indexed: 11/02/2023] Open
Abstract
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.
Collapse
Affiliation(s)
| | | | | | - Samantha Bove
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | | | | | | | | | | | | | - Lucia Rinaldi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | |
Collapse
|
8
|
Alagna L, Palomba E, Chatenoud L, Massafra R, Magni F, Mancabelli L, Donnini S, Elli F, Forastieri A, Gaipa G, Abbruzzese C, Fumagalli R, Munari M, Panacea A, Picetti E, Terranova L, Turroni F, Vaschetto R, Zoerle T, Citerio G, Gori A, Bandera A. 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. J Hosp Infect 2023; 140:90-95. [PMID: 37562590 DOI: 10.1016/j.jhin.2023.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
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.
Collapse
Affiliation(s)
- L Alagna
- Infectious Diseases Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - E Palomba
- Infectious Diseases Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
| | - L Chatenoud
- Laboratory of Clinical Epidemiology, Department of Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - R Massafra
- Infectious Diseases Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - F Magni
- Neurointensive Care Unit, ASST-Monza, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - L Mancabelli
- Department of Medicine and Surgery, University of Parma, Parma, Italy; Interdepartmental Research Centre 'Microbiome Research Hub', University of Parma, Parma, Italy
| | - S Donnini
- Department of Anaesthesia and Intensive Unit, Spedali Riuniti Livorno ATNO ESTAR, Livorno, Italy
| | - F Elli
- Department of Anaesthesia and Intensive Unit, Spedali Riuniti Livorno ATNO ESTAR, Livorno, Italy
| | - A Forastieri
- Department of Anaesthesia and Intensive Care, A. Manzoni Hospital, ASST Lecco, Lecco, Italy
| | - G Gaipa
- Tettamanti Research Centre, M.Tettamanti Foundation, Department of Paediatrics, University of Milano-Bicocca, Monza, Italy
| | - C Abbruzzese
- Department of Anaesthesia, Critical Care and Emergency, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - R Fumagalli
- Intensive Care, ASST GOM Niguarda, Milan, Italy
| | - M Munari
- Anaesthesia and Intensive Care Unit, University Hospital of Padova, Padova, Italy
| | - A Panacea
- Università degli Studi di Brescia, Brescia, Italy
| | - E Picetti
- Department of Anaesthesia and Intensive Care, Parma University Hospital, Parma, Italy
| | - L Terranova
- Internal Medicine Department, Respiratory Unit and Adult Cystic Fibrosis Centre, Foundation IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - F Turroni
- Interdepartmental Research Centre 'Microbiome Research Hub', University of Parma, Parma, Italy; Laboratory of Probiogenomics, Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - R Vaschetto
- Department of Anaesthesia and Intensive Unit, Ospedale Maggiore della Carità, Novara, Italy
| | - T Zoerle
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Neuroscience Intensive Care Unit, Department of Anaesthesia and Critical Care, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - G Citerio
- Neurointensive Care Unit, ASST-Monza, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy; School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy; Neurointensive Care Unit, Department of Neuroscience, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - A Gori
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Infectious Diseases, ASST Fatebenefratelli-Sacco University Hospital, Milan, Italy
| | - A Bandera
- Infectious Diseases Unit, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| |
Collapse
|
9
|
Comes MC, Arezzo F, Cormio G, Bove S, Calabrese A, Fanizzi A, Kardhashi A, La Forgia D, Legge F, Romagno I, Loizzi V, Massafra R. An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy. Front Oncol 2023; 13:1181792. [PMID: 37519818 PMCID: PMC10374844 DOI: 10.3389/fonc.2023.1181792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesca Arezzo
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica - (DiMePRe-J), Università di Bari “Aldo Moro”, Bari, Italy
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Gennaro Cormio
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Angela Calabrese
- Unità Operativa Semplice di Radiodiagnostica Avanzata, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Anila Kardhashi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesco Legge
- Unità di Ginecologia Oncologica, “F. Miulli” Ospedale Generale Regionale, Acquaviva delle Fonti, Bari, Italy
| | | | - Vera Loizzi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| |
Collapse
|
10
|
Fanizzi A, Pomarico D, Rizzo A, Bove S, Comes MC, Didonna V, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Rinaldi L, Tamborra P, Zito A, Lorusso V, Massafra R. Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer. Sci Rep 2023; 13:8575. [PMID: 37237020 DOI: 10.1038/s41598-023-35344-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
11
|
Fanizzi A, Graps E, Bavaro DA, Farella M, Bove S, Campobasso F, Comes MC, Cristofaro C, Forgia DL, Milella M, Iacovelli S, Villani R, Signorile R, De Bartolo A, Lorusso V, Massafra R. Assessing the cost-effectiveness of waiting list reduction strategies for a breast radiology department: a real-life case study. BMC Health Serv Res 2023; 23:526. [PMID: 37221516 PMCID: PMC10207781 DOI: 10.1186/s12913-023-09447-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 04/25/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND A timely diagnosis is essential for improving breast cancer patients' survival and designing targeted therapeutic plans. For this purpose, the screening timing, as well as the related waiting lists, are decisive. Nonetheless, even in economically advanced countries, breast cancer radiology centres fail in providing effective screening programs. Actually, a careful hospital governance should encourage waiting lists reduction programs, not only for improving patients care, but also for minimizing costs associated with the treatment of advanced cancers. Thus, in this work, we proposed a model to evaluate several scenarios for an optimal distribution of the resources invested in a Department of Breast Radiodiagnosis. MATERIALS AND METHODS Particularly, we performed a cost-benefit analysis as a technology assessment method to estimate both costs and health effects of the screening program, to maximise both benefits related to the quality of care and resources employed by the Department of Breast Radiodiagnosis of Istituto Tumori "Giovanni Paolo II" of Bari in 2019. Specifically, we determined the Quality-Adjusted Life Year (QALY) for estimating health outcomes, in terms of usefulness of two hypothetical screening strategies with respect to the current one. While the first hypothetical strategy adds one team made up of a doctor, a technician and a nurse, along with an ultrasound and a mammograph, the second one adds two afternoon teams. RESULTS This study showed that the most cost-effective incremental ratio could be achieved by reducing current waiting lists from 32 to 16 months. Finally, our analysis revealed that this strategy would also allow to include more people in the screening programs (60,000 patients in 3 years).
Collapse
Affiliation(s)
- Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Elisabetta Graps
- Direttore medico Area Valutazione e Ricerca, coordinatore del Centro regionale di Health Technology Assessment AReSS Puglia, Bari, Italy
| | | | - Marco Farella
- Dipartimento di Economia, Management e Diritto dell'Impresa, Università degli Studi di Bari "Aldo Moro", Largo Abbazia Santa Scolastica, 53, Bari, 70124, Italy
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Francesco Campobasso
- Dipartimento di Economia, Management e Diritto dell'Impresa, Università degli Studi di Bari "Aldo Moro", Largo Abbazia Santa Scolastica, 53, Bari, 70124, Italy
| | - Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Cristian Cristofaro
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Daniele La Forgia
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy.
| | - Martina Milella
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Serena Iacovelli
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Rossella Villani
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Rahel Signorile
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Alessio De Bartolo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, Bari, 70124, Italy
| |
Collapse
|
12
|
La Forgia D, Signorile R, Bove S, Arezzo F, Cormio G, Daniele A, Dellino M, Fanizzi A, Gatta G, Lafranceschina M, Massafra R, Rizzo A, Zito FA, Neri E, Faggioni L. Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results. Radiol Med 2023:10.1007/s11547-023-01640-7. [PMID: 37198373 DOI: 10.1007/s11547-023-01640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023]
Abstract
Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.
Collapse
Affiliation(s)
- Daniele La Forgia
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Rahel Signorile
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Samantha Bove
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Francesca Arezzo
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
- Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, 70121, Bari, Italy
| | - Gennaro Cormio
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
- Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, 70121, Bari, Italy
| | - Antonella Daniele
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miriam Dellino
- Clinic of Obstetrics and Gynecology, San Paolo Hospital, 70123, Bari, Italy
- Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70100, Bari, Italy
| | - Annarita Fanizzi
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy.
| | - Gianluca Gatta
- Breast Unit, Department of Clinical and Experimental Internship, University of Campania Luigi Vanvitelli, Via De Crecchio 7, 80138, Naples, Italy
| | - Miria Lafranceschina
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy.
| | - Alessandro Rizzo
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| |
Collapse
|
13
|
Bavaro DA, Fanizzi A, Iacovelli S, Bove S, Comes MC, Cristofaro C, Cutrignelli D, De Santis V, Nardone A, Lagattolla F, Rizzo A, Ressa CM, Massafra R. A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients. Healthcare (Basel) 2023; 11:healthcare11071042. [PMID: 37046969 PMCID: PMC10094026 DOI: 10.3390/healthcare11071042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing.
Collapse
Affiliation(s)
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Serena Iacovelli
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Cristian Cristofaro
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Daniela Cutrignelli
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Valerio De Santis
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annalisa Nardone
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Fulvia Lagattolla
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Alessandro Rizzo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Cosmo Maurizio Ressa
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| |
Collapse
|
14
|
Fioretti AM, Leopizzi T, La Forgia D, Scicchitano P, Oreste D, Fanizzi A, Massafra R, Oliva S. Incidental right atrial mass in a patient with secondary pancreatic cancer: A case report and review of literature. World J Clin Cases 2023; 11:1206-1216. [PMID: 36874413 PMCID: PMC9979295 DOI: 10.12998/wjcc.v11.i5.1206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 11/30/2022] [Accepted: 01/10/2023] [Indexed: 02/14/2023] Open
Abstract
BACKGROUND The incidental detection of a right atrial mass during routine cardioncological workup is a rare condition. The correct differential diagnosis between cancer and thrombi is challenging. A biopsy may not be feasible while diagnostic techniques and tools may not be available.
CASE SUMMARY We report the case of a 59-year-old female patient with a history of breast cancer and current secondary metastatic pancreatic cancer. She developed deep vein thrombosis and pulmonary embolism and was admitted to the Outpatient Clinic of our Cardio-Oncology Unit for follow-up. Transthoracic echocardiogram incidentally found a right atrial mass. Clinical management was difficult due to the abrupt worsening of the patient’s clinical condition and the progressive severe thrombocytopenia. We suspected a thrombus, according to its echocardiographic appearance, the patient’s cancer history and recent venous thromboembolism. The patient was unable to adhere to low molecular weight heparin treatment. Due to worsening prognosis, palliative care was recommended. We also highlighted the distinguishing features between thrombi and tumors. We proposed a diagnostic flowchart to aid diagnostic decision making in the case of an incidental atrial mass.
CONCLUSION This case report highlights the importance of cardioncological surveillance during anticancer treatments to detect cardiac masses.
Collapse
Affiliation(s)
| | - Tiziana Leopizzi
- Cardiology and Intensive Care Unit, Ospedale SS. Annunziata, Taranto 74121, Italy
| | - Daniele La Forgia
- Department of Radiology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari 70124, Italy
| | - Pietro Scicchitano
- Cardiology and Intensive Care Unit, Ospedale “Fabio Perinei”, Altamura (Bari) 70022, Italy
| | - Donato Oreste
- Department of Radiology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari 70124, Italy
| | - Annarita Fanizzi
- Department of Oncology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari 70124, Italy
| | - Raffaella Massafra
- Department of Oncology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari 70124, Italy
| | - Stefano Oliva
- Cardio-Oncology, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari 70124, Italy
| |
Collapse
|
15
|
Lagattolla F, Zanchi B, Pietro M, Cormio C, Lorusso V, Diotaiuti S, Fanizzi A, Massafra R, Costanzo S, Caporale F, Rieti E, Romito F. Receptive music therapy versus group music therapy with breast cancer patients hospitalized for surgery. Support Care Cancer 2023; 31:162. [PMID: 36781543 PMCID: PMC9924845 DOI: 10.1007/s00520-023-07624-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/01/2023] [Indexed: 02/15/2023]
Abstract
Hospitalization for breast surgery is a distressing experience for women. This study investigated the impact of music therapy (MT), an integrative approach that is characterized by the establishment of a therapeutic relationship between patients and a certified music therapist, through different musical interventions targeted to the specific needs of the patients. The impact of two different MT experiences was compared on anxiety and distressing emotions. METHODS One hundred fifty-one patients during hospitalization for breast surgery were randomly assigned to two music therapy treatment arms: individual/receptive (MTri) vs. group/active-receptive integrated (MTiGrp). Stress, depression, anger, and need for help were measured with the emotion thermometers (ET) and State Trait Anxiety Inventory Y-1 form (STAY-Y1). Data were collected before and after the MT intervention. RESULTS Both types of MT interventions were effective in reducing all the variables: stress, depression, anger, and anxiety (T Student p‹0.01). Patients' perception of help received was correlated with a significant reduction in anxiety and distressing emotions during hospitalization for breast surgery. CONCLUSION Considerations regarding the implementation of MT interventions in clinical practice are discussed. In individual receptive MT, there was a significant decrease in anxiety levels, whereas in the integrated MT group, there was a higher perception of help received and use of inter-individual resources.
Collapse
Affiliation(s)
- Fulvia Lagattolla
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Barbara Zanchi
- Department of Music Therapy, Conservatorio Di Musica “Bruno Maderna”, Cesena, Italy
| | - Milella Pietro
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Direzione Sanitaria, IRCCS Istituto Tumori Giovanni Paolo II, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Claudia Cormio
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa Di Oncologia Medica, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Sergio Diotaiuti
- Unità Operativa Complessa Di Chirurgica Senologica Plastica E Ricostruttiva, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Silvia Costanzo
- Oncologia sperimentale - Centro Studi Tumori Eredo-Familiari, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesca Caporale
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Erika Rieti
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Francesca Romito
- Servizio Di Psiconcologia, IRCCS Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| |
Collapse
|
16
|
Massafra R, Fanizzi A, Amoroso N, Bove S, Comes MC, Pomarico D, Didonna V, Diotaiuti S, Galati L, Giotta F, La Forgia D, Latorre A, Lombardi A, Nardone A, Pastena MI, Ressa CM, Rinaldi L, Tamborra P, Zito A, Paradiso AV, Bellotti R, Lorusso V. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence. Front Med (Lausanne) 2023; 10:1116354. [PMID: 36817766 PMCID: PMC9932275 DOI: 10.3389/fmed.2023.1116354] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/13/2023] [Indexed: 02/05/2023] Open
Abstract
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.
Collapse
Affiliation(s)
| | | | - Nicola Amoroso
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Domenico Pomarico
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | | | | | - Luisa Galati
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Angela Lombardi
- Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Bari, Italy
| | | | | | | | - Lucia Rinaldi
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Roberto Bellotti
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Vito Lorusso
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| |
Collapse
|
17
|
Rizzo A, Ricci AD, Fanizzi A, Massafra R, De Luca R, Brandi G. Immune-Based Combinations versus Sorafenib as First-Line Treatment for Advanced Hepatocellular Carcinoma: A Meta-Analysis. Curr Oncol 2023; 30:749-757. [PMID: 36661706 PMCID: PMC9858216 DOI: 10.3390/curroncol30010057] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
Recent years have observed the emergence of novel therapeutic opportunities for advanced hepatocellular carcinoma (HCC), such as combination therapies including immune checkpoint inhibitors. We performed a meta-analysis with the aim to compare median overall survival (OS), median progression-free survival (PFS), complete response (CR) rate, and partial response (PR) rate in advanced HCC patients receiving immune-based combinations versus sorafenib. A total of 2176 HCC patients were available for the meta-analysis (immune-based combinations = 1334; sorafenib = 842) and four trials were included. Immune-based combinations decreased the risk of death by 27% (HR, 0.73; 95% CI, 0.65−0.83; p < 0.001); similarly, a PFS benefit was observed (HR, 0.64; 95% CI, 0.5−0.84; p < 0.001). In addition, immune-based combinations showed better CR rate and PR rate, with ORs of 12.4 (95% CI, 3.02−50.85; p < 0.001) and 3.48 (95% CI, 2.52−4.8; p < 0.03), respectively. The current study further confirms that first-line immune-based combinations have a place in the management of HCC. The CR rate observed in HCC patients receiving immune-based combinations appears more than twelve times higher compared with sorafenib monotherapy, supporting the long-term benefit of these combinatorial strategies, with even the possibility to cure advanced disease.
Collapse
Affiliation(s)
- Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Angela Dalia Ricci
- Medical Oncology Unit, National Institute of Gastroenterology, “Saverio de Bellis” Research Hospital, 70013 Castellana Grotte, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaele De Luca
- Department of Surgical Oncology, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy
| | - Giovanni Brandi
- Department of Specialized, Experimental and Diagnostic Medicine, University of Bologna, Via Giuseppe Massarenti, 9, 40138 Bologna, Italy
- Division of Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni, 15, 40138 Bologna, Italy
| |
Collapse
|
18
|
Rescigno M, Agrati C, Salvarani C, Giannarelli D, Costantini M, Mantovani A, Massafra R, Zinzani PL, Morrone A, Notari S, Matusali G, Pinter GL, Uccelli A, Ciliberto G, Baldanti F, Locatelli F, Silvestris N, Sinno V, Turola E, Lupo-Stanghellini MT, Apolone G. Neutralizing antibodies to Omicron after the fourth SARS-CoV-2 mRNA vaccine dose in immunocompromised patients highlight the need of additional boosters. Front Immunol 2023; 14:1104124. [PMID: 36776853 PMCID: PMC9911671 DOI: 10.3389/fimmu.2023.1104124] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/09/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Immunocompromised patients have been shown to have an impaired immune response to COVID-19 vaccines. Methods Here we compared the B-cell, T-cell and neutralizing antibody response to WT and Omicron BA.2 SARS-CoV-2 virus after the fourth dose of mRNA COVID-19 vaccines in patients with hematological malignancies (HM, n=71), solid tumors (ST, n=39) and immune-rheumatological (IR, n=25) diseases. The humoral and T-cell responses to SARS-CoV-2 vaccination were analyzed by quantifying the anti-RBD antibodies, their neutralization activity and the IFN-γ released after spike specific stimulation. Results We show that the T-cell response is similarly boosted by the fourth dose across the different subgroups, while the antibody response is improved only in patients not receiving B-cell targeted therapies, independent on the pathology. However, 9% of patients with anti-RBD antibodies did not have neutralizing antibodies to either virus variants, while an additional 5.7% did not have neutralizing antibodies to Omicron BA.2, making these patients particularly vulnerable to SARS-CoV-2 infection. The increment of neutralizing antibodies was very similar towards Omicron BA.2 and WT virus after the third or fourth dose of vaccine, suggesting that there is no preferential skewing towards either virus variant with the booster dose. The only limited step is the amount of antibodies that are elicited after vaccination, thus increasing the probability of developing neutralizing antibodies to both variants of virus. Discussion These data support the recommendation of additional booster doses in frail patients to enhance the development of a B-cell response directed against Omicron and/or to enhance the T-cell response in patients treated with anti-CD20.
Collapse
Affiliation(s)
- Maria Rescigno
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,Mucosal Immunology and Microbiota Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, Rozzano, Milano, Italy
| | - Chiara Agrati
- Cellular Immunology Laboratory, National Institute for Infectious Diseases (INMI) L Spallanzani - Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.,Department of Hematology and Oncology and Cell and Gene Therapy, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Bambino Gesù Children Hospital , Roma, Italy
| | - Carlo Salvarani
- Unità di Reumatologia, Azienda Unità Sanitaria Locale-Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) di Reggio Emilia, Reggio Emilia, Italy.,Unità di Reumatologia, Università degli Studi di Modena e Reggio Emilia, Reggio Emilia, Italy
| | - Diana Giannarelli
- Facility di Epidemiologia e Biostatistica, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Roma, Italy
| | - Massimo Costantini
- Scientific Directorate, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Alberto Mantovani
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,Humanitas Scientific Directorate, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, Rozzano, Milan, Italy.,William Harvey Research Institute, Queen Mary University, London, United Kingdom
| | - Raffaella Massafra
- Vice Scientific Directorate, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Pier Luigi Zinzani
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli", Bologna, Italy.,Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Aldo Morrone
- Scientific Directorate, San Gallicano Dermatological Institute Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Roma, Italy
| | - Stefania Notari
- Cellular Immunology Laboratory, National Institute for Infectious Diseases (INMI) L Spallanzani - Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giulia Matusali
- Virology Laboratory, INMI L Spallanzani - Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Roma, Italy
| | - Giuseppe Lauria Pinter
- Scientific Directorate, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Neurologico Carlo Besta, Milano, Italy.,Department of Medical Biotechnology and Translational Medicine, University of Milan, Milano, Italy
| | - Antonio Uccelli
- Scientific Directorate, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Gennaro Ciliberto
- Scientific Directorate, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Regina Elena, National Cancer Institute, Istituti Fisioterapici Ospitalieri (IFO), Roma, Italy
| | - Fausto Baldanti
- Microbiology and Virology Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Matteo, Pavia, Italy.,Department of Clinical, Surgical, Diagnostics and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Franco Locatelli
- Department of Hematology and Oncology and Cell and Gene Therapy, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Bambino Gesù Children Hospital , Roma, Italy.,Department of Pediatrics, Catholic University of the Sacred Heart, Roma, Italy
| | - Nicola Silvestris
- Medical Oncology Unit, Department of Human Pathology "G. Barresi", University of Messina, Messina, Italy
| | - Valentina Sinno
- Department of Oncology and Hematology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Elena Turola
- Infrastruttura Ricerca e Statistica, Azienda Unità Sanitaria Locale-Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) di Reggio Emilia, Reggio Emilia, Italy
| | - Maria Teresa Lupo-Stanghellini
- Hematology and BMT Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale San Raffaele, Milano, Italy
| | - Giovanni Apolone
- Scientific Directorate, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | | |
Collapse
|
19
|
Bove S, Fanizzi A, Fadda F, Comes MC, Catino A, Cirillo A, Cristofaro C, Montrone M, Nardone A, Pizzutilo P, Tufaro A, Galetta D, Massafra R. A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region. PLoS One 2023; 18:e0285188. [PMID: 37130116 PMCID: PMC10153708 DOI: 10.1371/journal.pone.0285188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.
Collapse
Affiliation(s)
- Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | - Federico Fadda
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | | - Angelo Cirillo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | | | | | | - Antonio Tufaro
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | |
Collapse
|
20
|
La Forgia D, Paparella G, Signorile R, Arezzo F, Comes MC, Cormio G, Daniele A, Fanizzi A, Fioretti AM, Gatta G, Lafranceschina M, Rizzo A, Zaccaria GM, Rosa A, Massafra R. Lean Perspectives in an Organizational Change in a Scientific Direction of an Italian Research Institute: Experience of the Cancer Institute of Bari. Int J Environ Res Public Health 2022; 20:239. [PMID: 36612562 PMCID: PMC9819426 DOI: 10.3390/ijerph20010239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Lean management is a relatively new organizational vision transferred from the automotive industry to the healthcare and administrative sector based on analyzing a production process to emphasize value and reduce waste. This approach is particularly interesting in a historical moment of cuts and scarcity of economic resources and could represent a low-cost organizational solution in many production companies. In this work, we analyzed the presentation and the initial management of current ministerial research projects up to the approval by the Scientific Directorate of an Italian research institute. Furthermore, the initial mode in 2021 ("as is") and the potential mode ("to be") according to a Lean model are studied, according to the current barriers highlighted by the final users of the process and carrying out some perspective analyses with some reference indicators.
Collapse
Affiliation(s)
- Daniele La Forgia
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gaetano Paparella
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Rahel Signorile
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Francesca Arezzo
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gennaro Cormio
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Department of Interdisciplinary Medicine, University of Bari, 70124 Bari, Italy
| | - Antonella Daniele
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Agnese Maria Fioretti
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Miria Lafranceschina
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Alessandro Rizzo
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gian Maria Zaccaria
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Angelo Rosa
- Department of Management, Finance and Technology, LUM University, 70010 Casamassima, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| |
Collapse
|
21
|
Comes MC, Fucci L, Mele F, Bove S, Cristofaro C, De Risi I, Fanizzi A, Milella M, Strippoli S, Zito A, Guida M, Massafra R. A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients. Sci Rep 2022; 12:20366. [PMID: 36437296 PMCID: PMC9701687 DOI: 10.1038/s41598-022-24315-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/14/2022] [Indexed: 11/28/2022] Open
Abstract
The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.
Collapse
Affiliation(s)
- Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Livia Fucci
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Fabio Mele
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Cristian Cristofaro
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Ivana De Risi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Martina Milella
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Sabino Strippoli
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Alfredo Zito
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Michele Guida
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| |
Collapse
|
22
|
Fanizzi A, Scognamillo G, Nestola A, Bambace S, Bove S, Comes MC, Cristofaro C, Didonna V, Di Rito A, Errico A, Palermo L, Tamborra P, Troiano M, Parisi S, Villani R, Zito A, Lioce M, Massafra R. Corrigendum: Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer. Front Med (Lausanne) 2022; 9:1089705. [PMID: 36482915 PMCID: PMC9723445 DOI: 10.3389/fmed.2022.1089705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 01/25/2023] Open
Abstract
[This corrects the article DOI: 10.3389/fmed.2022.993395.].
Collapse
Affiliation(s)
| | | | | | - Santa Bambace
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy,*Correspondence: Samantha Bove
| | | | | | | | | | - Angelo Errico
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | | | | | - Michele Troiano
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | - Salvatore Parisi
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Marco Lioce
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | |
Collapse
|
23
|
Fanizzi A, Scognamillo G, Nestola A, Bambace S, Bove S, Comes MC, Cristofaro C, Didonna V, Di Rito A, Errico A, Palermo L, Tamborra P, Troiano M, Parisi S, Villani R, Zito A, Lioce M, Massafra R. Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer. Front Med (Lausanne) 2022; 9:993395. [PMID: 36213659 PMCID: PMC9537690 DOI: 10.3389/fmed.2022.993395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/01/2022] [Indexed: 11/23/2022] Open
Abstract
Background and purpose Although the latest breakthroughs in radiotherapy (RT) techniques have led to a decrease in adverse event rates, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers could be useful to predict the toxicity risk related to each individual patient. Our preliminary work aims to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk in 3 months after RT in patients with oropharyngeal cancer (OPC). Materials and methods We performed a multicenter data collection. We enrolled 61 patients referred to three care centers in Apulia, Italy, out of which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and alcohol-smoking habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network (CNN) architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on “fake” parotid contours. Results The best performances were achieved by the model exploiting the radiomic features alone. On the independent test, the model reached median AUC, accuracy, sensitivity, and specificity values of 81.17, 83.33, 71.43, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures. Conclusion Radiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care.
Collapse
Affiliation(s)
| | | | | | - Santa Bambace
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
- *Correspondence: Samantha Bove,
| | | | | | | | | | - Angelo Errico
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | | | | | - Michele Troiano
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | - Salvatore Parisi
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
| | - Marco Lioce
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
| | | |
Collapse
|
24
|
Massafra R, Comes MC, Bove S, Didonna V, Diotaiuti S, Giotta F, Latorre A, La Forgia D, Nardone A, Pomarico D, Ressa CM, Rizzo A, Tamborra P, Zito A, Lorusso V, Fanizzi A. A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification. PLoS One 2022; 17:e0274691. [PMID: 36121822 PMCID: PMC9484691 DOI: 10.1371/journal.pone.0274691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/02/2022] [Indexed: 12/24/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- * E-mail: (MCC); (SB)
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- * E-mail: (MCC); (SB)
| | | | | | | | - Agnese Latorre
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | | | - Domenico Pomarico
- Dipartimento di Fisica and MECENAS, Università di Bari, Bari, Italy
- INFN, Sezione di Bari, Bari, Italy
| | | | | | | | - Alfredo Zito
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | |
Collapse
|
25
|
Massafra R, Comes MC, Bove S, Didonna V, Gatta G, Giotta F, Fanizzi A, La Forgia D, Latorre A, Pastena MI, Pomarico D, Rinaldi L, Tamborra P, Zito A, Lorusso V, Paradiso AV. Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J Pers Med 2022; 12:jpm12060953. [PMID: 35743737 PMCID: PMC9225219 DOI: 10.3390/jpm12060953] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 02/04/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
- Correspondence: (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Correspondence: (A.F.); (D.L.F.)
| | - Agnese Latorre
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Angelo Virgilio Paradiso
- Oncologia Sperimentale e Biobanca, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| |
Collapse
|
26
|
Rizzo A, Cusmai A, Massafra R, Bove S, Comes MC, Fanizzi A, Rinaldi L, Acquafredda S, Gadaleta-Caldarola G, Oreste D, Zito A, Giotta F, Lorusso V, Palmiotti G. Pathological Complete Response to Neoadjuvant Chemoimmunotherapy for Early Triple-Negative Breast Cancer: An Updated Meta-Analysis. Cells 2022; 11:cells11121857. [PMID: 35740985 PMCID: PMC9221459 DOI: 10.3390/cells11121857] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 12/12/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (L.R.); (S.A.); (G.P.)
- Correspondence: ; Tel.: +39-051-2144078; Fax: +39-051-6364037
| | - Antonio Cusmai
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (L.R.); (S.A.); (G.P.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (S.B.); (M.C.C.); (A.F.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (S.B.); (M.C.C.); (A.F.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (S.B.); (M.C.C.); (A.F.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (S.B.); (M.C.C.); (A.F.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (L.R.); (S.A.); (G.P.)
| | - Silvana Acquafredda
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (L.R.); (S.A.); (G.P.)
| | - Gennaro Gadaleta-Caldarola
- Medical Oncology Unit, ‘Mons. R. Dimiccoli’ Hospital, Barletta (BT), Azienda Sanitaria Locale Barletta, 76121 Barletta, Italy;
| | - Donato Oreste
- Radiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Giovanni Paolo II, 70124 Bari, Italy;
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Gennaro Palmiotti
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, IRCCS, Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (L.R.); (S.A.); (G.P.)
| |
Collapse
|
27
|
Rizzo A, Cusmai A, Massafra R, Bove S, Comes MC, Fanizzi A, Gadaleta-Caldarola G, Oreste D, Zito A, Giotta F, Lorusso V, Palmiotti G. Systemic Treatments for Metastatic Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: Old Certainties and New Frontiers. Cancer Control 2022. [PMCID: PMC9160897 DOI: 10.1177/10732748221106267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Epidermal growth factor receptor 2 (EGFR2, also known as HER2) overexpression and/or amplification confers a more aggressive clinical behavior but also represents a therapeutic opportunity for targeted therapies in breast cancer (BC). Over the last 2 decades, the prognosis of HER2-positive metastatic BC patients has improved due to the introduction of anti-HER2 agents including trastuzumab and novel, emerging drugs and combinations such as trastuzumab deruxtecan and tucatinib – trastuzumab - capecitabine. Herein, we provide a critical overview of current clinical recommendations and emerging treatment options for metastatic HER2-positive BC, especially focusing on recently presented and published clinical trials in this setting.
Collapse
Affiliation(s)
- Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Antonio Cusmai
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Gennaro Gadaleta-Caldarola
- Medical Oncology Unit, ‘Mons. R. Dimiccoli’ Hospital, Barletta, Azienda Sanitaria Locale Barletta, Italy
| | - Donato Oreste
- Radiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| | - Gennaro Palmiotti
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco, Bari, Italy
| |
Collapse
|
28
|
Rizzo A, Massafra R, Fanizzi A, Rinaldi L, Cusmai A, Latorre A, Zaccaria GM, Ronchi M, Telegrafo M, Gadaleta-Caldarola G, Giotta F, Lorusso V, Palmiotti G. Adenosine pathway inhibitors: novel investigational agents for the treatment of metastatic breast cancer. Expert Opin Investig Drugs 2022; 31:707-713. [DOI: 10.1080/13543784.2022.2078191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Antonio Cusmai
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gian Maria Zaccaria
- Unit of Hematology and Cell Therapy, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy
| | - Maria Ronchi
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Michele Telegrafo
- DETO, Department of Emergency and Organ Transplantations, Breast Care Unit, Aldo Moro University of Bari Medical School, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Gennaro Gadaleta-Caldarola
- Medical Oncology Unit, ‘Mons. R. Dimiccoli’ Hospital, Barletta (BT), Azienda Sanitaria Locale Barletta, 76121, Italy
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| | - Gennaro Palmiotti
- Struttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello”, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
| |
Collapse
|
29
|
Bove S, Comes MC, Lorusso V, Cristofaro C, Didonna V, Gatta G, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Pomarico D, Rinaldi L, Tamborra P, Zito A, Fanizzi A, Massafra R. A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients. Sci Rep 2022; 12:7914. [PMID: 35552476 PMCID: PMC9098914 DOI: 10.1038/s41598-022-11876-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/29/2022] [Indexed: 12/19/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Samantha Bove
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Cristian Cristofaro
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Gianluca Gatta
- Dipartimento Di Medicina Di Precisione, Università Della Campania "Luigi Vanvitelli", 80131, Napoli, Italy
| | - Francesco Giotta
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale Di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Agnese Latorre
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale Di Oncologia Per La Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
30
|
Massafra R, Catino A, Perrotti PMS, Pizzutilo P, Fanizzi A, Montrone M, Galetta D. Informative Power Evaluation of Clinical Parameters to Predict Initial Therapeutic Response in Patients with Advanced Pleural Mesothelioma: A Machine Learning Approach. J Clin Med 2022; 11:jcm11061659. [PMID: 35329985 PMCID: PMC8950691 DOI: 10.3390/jcm11061659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/10/2022] Open
Abstract
Malignant pleural mesothelioma (MPM) is a rare neoplasm whose early diagnosis is challenging and systemic treatments are generally administered as first line in the advanced disease stage. The initial clinical response may represent a useful parameter in terms of identifying patients with a better long-term outcome. In this report, the initial therapeutical response in 46 patients affected with advanced/unresectable pleural mesothelioma was investigated. The initial therapeutic response was assessed by CT scan and clinical examination after 2–3 treatment cycles. Our preliminary evaluation shows that the group of patients treated with regimens including antiangiogenetics and/or immunotherapy had a significantly better initial response as compared to patients only treated with standard chemotherapy, exhibiting a disease control rate (DCR) of 100% (95% IC, 79.40–100%) and 80.0% (95% IC, 61.40–92.30%), respectively. Furthermore, the therapeutic response was correlated with the disease stage, blood leukocytes and neutrophils, high albumin serum levels, and basal body mass index (BMI). Specifically, the patients with disease stage III showed a DCR of 95.7% (95% IC, 78.1–99.9%), whereas for disease stage IV the DCR decreased to 66.7% (95% IC, 34.9–9.1%). Moreover, a better initial response was observed in patients with a higher BMI, who reached a DCR of 96.10% (95% IC, 80.36–99.90%). Furthermore, in order to evaluate in the predictive power of the collected features a multivariate way, we report the preliminary results of a machine learning model for predicting the initial therapeutic response. We trained a state-of-the-art algorithm combined to a sequential forward feature selection procedure. The model reached a median AUC value, accuracy, sensitivity, and specificity of 77.0%, 75%, 74.8%, and 83.3%, respectively. The features with greater informational power were gender, histotype, BMI, smoking habits, packs/year, and disease stage. Our preliminary data support the possible favorable correlation between innovative treatments and therapeutic response in patients with unresectable/advanced pleural mesothelioma. The small sample size does not allow concrete conclusions to be drawn; nevertheless, this work is the basis of an ongoing study that will also involve radiomics in a larger dataset.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annamaria Catino
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Pia Maria Soccorsa Perrotti
- Struttura Semplice Dipartimentale di Radiologia, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pamela Pizzutilo
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
- Correspondence: ; Tel.: +39-080-555-5111
| | - Michele Montrone
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| | - Domenico Galetta
- Struttura Semplice Dipartimentale di Oncologia Medica per la Patologia Toracica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.C.); (P.P.); (M.M.); (D.G.)
| |
Collapse
|
31
|
Strippoli S, Fanizzi A, Quaresmini D, Nardone A, Armenio A, Figliuolo F, Filotico R, Fucci L, Mele F, Traversa M, De Luca F, Montagna ES, Ruggieri E, Ferraiuolo S, Macina F, Tommasi S, Sciacovelli AM, De Risi I, Albano A, Massafra R, Guida M. Cemiplimab in an Elderly Frail Population of Patients With Locally Advanced or Metastatic Cutaneous Squamous Cell Carcinoma: A Single-Center Real-Life Experience From Italy. Front Oncol 2021; 11:686308. [PMID: 34820323 PMCID: PMC8606572 DOI: 10.3389/fonc.2021.686308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/11/2021] [Indexed: 12/19/2022] Open
Abstract
Background Cutaneous squamous cell carcinoma (CSCC) is the second most common skin cancer whose incidence is growing parallel to the lengthening of the average lifespan. Cemiplimab, an antiPD-1 monoclonal antibody, is the first approved immunotherapy for patients with locally advanced CSCC (laCSCC) or metastatic CSCC (mCSCC) thanks to phase I and II studies showing high antitumor activity and good tolerability. Nevertheless, at present, very few data are available regarding cemiplimab in real-life experience and in frail, elderly, and immunosuppressed patients as well as regarding biomarkers able to predict response so as to guide therapeutic choices. Patients and Methods We built a retroprospective cohort study including 30 non-selected patients with laCSCC (25) and mCSCC (five) treated with cemiplimab from August 2019 to November 2020. Clinical outcomes, toxicity profile, and correlations with disease, patients, and peripheral blood parameters are explored. Results The median age was 81 years (range, 36-95), with 24 males and five patients having an immunosuppressive condition, while the frailty prevalence was 83% based on index derived from age, Eastern Cooperative Oncology Group performance status, and Charlson Comorbidity Index. We reported 23 responses (76.7%) with nine complete responses (30%). A statistically significant higher response rate was observed in head and neck primary tumors and in patients with hemoglobin level >12 g/dl. No difference was observed with respect to frailty, median age, sex, and body mass index. The baseline low neuthophil/lymphocyte ratio and low platelet/lymphocyte ratio resulted to be also correlated with a better response. Moreover, lymphocyte, neutrophil, and monocyte behaviors had an opposite trend in responders and non-responders. An overall response was reported in four of five immunosuppressed patients. Seventeen patients (57.6%) have an ongoing response and are still alive. Six responders had interrupted treatment (two for toxicity and four for personal choice) but maintained their response. The treatment was well tolerated by the majority of patients. The most common adverse events were fatigue in seven patients (23.3%) and skin toxicity in 10 patients (33.3%), including pruritus in six patients, rash in three patients, and bullous erythema in one patient. Conclusions In our real-life experience, cemiplimab showed a high antitumor activity with acceptable safety profile similar to those in trials with selected patients. Moreover, its antitumor activity resulted to be not impaired in very elderly patients and in those with immunocompromised status.
Collapse
Affiliation(s)
- Sabino Strippoli
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Annarita Fanizzi
- Health Physics Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Davide Quaresmini
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Annalisa Nardone
- Radiotherapy Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Andrea Armenio
- Plastic Surgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Francesco Figliuolo
- Plastic Surgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Raffaele Filotico
- Dermatology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Livia Fucci
- Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Fabio Mele
- Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Michele Traversa
- Radiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Federica De Luca
- Radiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Elisabetta Sara Montagna
- Medical Oncology Unit "Don Tonino Bello", Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Eustachio Ruggieri
- General Surgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Simona Ferraiuolo
- Pharmacy Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Francesco Macina
- Interventional and Medical Oncology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Stefania Tommasi
- Pharmacogenetics and Molecular Diagnostic Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Angela Monica Sciacovelli
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Ivana De Risi
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Anna Albano
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Raffaella Massafra
- Health Physics Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Michele Guida
- Rare Tumors and Melanoma Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, Bari, Italy
| |
Collapse
|
32
|
Gatta G, La Forgia D, Fanizzi A, Massafra R, Somma F, Belfiore MP, Pacella D, Cappabianca S, Salvia AAH. Prevalence of Patients Affected by Fibromyalgia in a Cohort of Women Underwent Mammography Screening. Healthcare (Basel) 2021; 9:1340. [PMID: 34683021 PMCID: PMC8544442 DOI: 10.3390/healthcare9101340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/11/2021] [Accepted: 09/26/2021] [Indexed: 11/17/2022] Open
Abstract
Fibromyalgia is a widespread condition which is currently underdiagnosed; therefore we conceived this study in order to assess whether a diagnostic suspicion may be assumed during widespread screening procedures, so that patients for which a reasonable diagnostic suspicion exist may be redirected towards rheumatologic evaluation. We analyzed a sample of 1060 patients, all of whom were female and undergoing standard breast cancer screening procedures, and proceeded to evaluate the level of pain they endured during mammographic exam. We also acquired a range of other information which we related to the level of pain endured; we suggested a rheumatologic examination for those patients who endured the highest level of pain and then we evaluated how many patients in this subgroup were actually diagnosed with fibromyalgia. Out of the 1060 patients who participated to our study, 139 presented level 4 pain intensity; One patient did not go for rheumatologic examination; the remaining 138 underwent rheumatologic evaluation, and 50 (36%, 28-44, 95% CI) were diagnosed with fibromyalgia. Our study shows that assessing the level of pain endured by patients during standard widespread screening procedures may be an effective asset in deciding whether or not to suggest specialist rheumatologic evaluation for fibromyalgia.
Collapse
Affiliation(s)
- Gianluca Gatta
- Dipartimento di Medicina di Precisione Università Della Campania “Luigi Vanvitelli”, 80127 Napoli, Italy; (G.G.); (M.P.B.); (S.C.); (A.A.H.S.)
| | - Daniele La Forgia
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Annarita Fanizzi
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Raffaella Massafra
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | | | - Maria Paola Belfiore
- Dipartimento di Medicina di Precisione Università Della Campania “Luigi Vanvitelli”, 80127 Napoli, Italy; (G.G.); (M.P.B.); (S.C.); (A.A.H.S.)
| | - Daniela Pacella
- Dipartimento Sanità Pubblica, Università degli Studi di Napoli “Federico II”, 80127 Napoli, Italy;
| | - Salvatore Cappabianca
- Dipartimento di Medicina di Precisione Università Della Campania “Luigi Vanvitelli”, 80127 Napoli, Italy; (G.G.); (M.P.B.); (S.C.); (A.A.H.S.)
| | - Antonio Alessandro Heliot Salvia
- Dipartimento di Medicina di Precisione Università Della Campania “Luigi Vanvitelli”, 80127 Napoli, Italy; (G.G.); (M.P.B.); (S.C.); (A.A.H.S.)
| |
Collapse
|
33
|
Gatta G, Cappabianca S, La Forgia D, Massafra R, Fanizzi A, Cuccurullo V, Brunese L, Tagliafico A, Grassi R. Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates. J Pers Med 2021; 11:jpm11090875. [PMID: 34575652 PMCID: PMC8468126 DOI: 10.3390/jpm11090875] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 12/22/2022] Open
Abstract
In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system.
Collapse
Affiliation(s)
- Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Salvatore Cappabianca
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Daniele La Forgia
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
- Correspondence: ; Tel.: +39-80-5555111
| | - Raffaella Massafra
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Annarita Fanizzi
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Vincenzo Cuccurullo
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Luca Brunese
- Dipartimento di Medicina e Scienze della Salute “Vincenzo Tiberio”—Università degli Studi del Molise, 86100 Campobasso, Italy;
| | | | - Roberto Grassi
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| |
Collapse
|
34
|
Guida M, Fanizzi A, Quaresmini D, Nardone A, Armenio A, Montagna ES, Sciacovelli AM, Massafra R, De Risi I, Albano A, Strippoli S. Cemiplimab in a very frail population of patients with advanced or metastatic cutaneous squamous cell carcinoma: A monocenter real-life experience from Italy. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.e21524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e21524 Background: Cutaneous squamous cell carcinoma (CSCC) is the second most common skin cancer. Although representing less than 5% of all CSCCs, advanced stages are difficult to treat. Cemiplimab, an antiPD-1 monoclonal antibody, is the first approved immunotherapy in the US and EU for patients with locally advanced (laCSCC) or metastatic (mCSCC) CSCC. Phase I-II studies showed high antitumor activity and good tolerability, but few data are still available regarding cemiplimab in real life experience in non-selected patients. Methods: We recruited 30 consecutive patients with laCSCC (25 pts) and mCSCC (5 pts) treated with cemiplimab from August 2019 to November 2020 at our Institution. Median age was 81 years (range 36-95); 24 males; median ECOG PS 1 (range 0-2). Five patients had an immunosuppressive condition including 3 patients with stable hematologic malignancies and two patients on immunosuppressive therapy for kidney transplantation and Crohn’s disease, respectively. The majority of patients had comorbidities (median 3). Cemiplimab was administered at the flat dose of 350 mg i.v. every 21 days until disease progression or unacceptable toxicity. In all patients we evaluated clinical outcomes, toxicity, and associations between clinical outcomes and peripheral blood parameters. Results: We reported 23 responses (ORR 76.7%) with CR in 5 patients (16.7%). One patient had SD for 5 months. The global DCR was 80%. The median duration of response and PFS was not reached at a median follow-up of 6 months. We observed a higher ORR in head and neck primary tumours (87% vs. 42.9% of others, p = 0.016) and in patients with haemoglobin level > 12 g/dL (87.5% vs. 64.3%). No significative difference in ORR was observed with respect to the median age (81.3% in >81 years vs. 71.4% in < 81 years). Among the 5 patients with immunosuppressive status, a response was obtained in 4 patients (80%), including 1 CR. Nine patients died, 7 for PD and 2 for causes unrelated to the disease. Twenty patients (67.7%) still have an ongoing response. The treatment was well tolerated by the majority of patients. The most common adverse events were fatigue in 7 patients (23.3%) and skin toxicity in 10 patients (33.3%) including pruritus in 6 patients, rash in 3 patients, bullous erythema in 1 patient. Only 3 (10%) patients experienced severe (grade 3/4) toxicity. Three responder patients interrupted treatment (2 for toxicity after 7 and 9 cycles, and one for pre-existing dementia) but maintaining their response. Conclusions: In our real-life experience cemiplimab showed high antitumor activity with acceptable safety profile similar to those in selected patients of trials. Moreover, its antitumor activity resulted not impaired in very elderly patients or in those with immunocompromized status.
Collapse
Affiliation(s)
- Michele Guida
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annarita Fanizzi
- Health Physics Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Davide Quaresmini
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annalisa Nardone
- Radiotherapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Andrea Armenio
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Elisabetta Sara Montagna
- Medical Oncology "Don Tonino Bello" Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | - Raffaella Massafra
- Health Physics Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Ivana De Risi
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Anna Albano
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Sabino Strippoli
- Rare Tumors and Melanoma Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| |
Collapse
|
35
|
Comes MC, La Forgia D, Didonna V, Fanizzi A, Giotta F, Latorre A, Martinelli E, Mencattini A, Paradiso AV, Tamborra P, Terenzio A, Zito A, Lorusso V, Massafra R. Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs. Cancers (Basel) 2021; 13:2298. [PMID: 34064923 PMCID: PMC8151784 DOI: 10.3390/cancers13102298] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/05/2021] [Accepted: 05/08/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.
Collapse
Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Eugenio Martinelli
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Arianna Mencattini
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Angelo Virgilio Paradiso
- Oncologia Medica Sperimentale, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Antonella Terenzio
- Unità di Oncologia Medica, Università Campus Bio-Medico, 00128 Roma, Italy;
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| |
Collapse
|
36
|
Massafra R, Pomarico D, Fanizzi A, Campobasso F, Didonna V, Latorre A, Nardone A, Pastena IM, Tamborra P, Lorusso V, La#Forgia D. Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients. J BUON 2021; 26:720-727. [PMID: 34268926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
PURPOSE Sentinel lymph node biopsy (SLNB) is an invasive surgical procedure and although it has fewer complications and is less severe than axillary lymph node dissection, it is not a risk-free procedure. Large prospective trials have documented SLNB that it is considered non-therapeutic in early stage breast cancer. METHODS Web-calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumour size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor. We collected 595 patients referred to our Institute resulting clinically negative T1 breast cancer characterized by sentinel lymph node status, prognostic factors defined by CM and also HER2 and Ki-67. We have compared classification performances obtained by online CM application with those obtained after training its algorithm on our database. RESULTS By training CM model on our dataset and using the same feature, adding HER2 or ki67 we reached a sensitivity median value of 71.4%, 73%, 70.4%, respectively, whereas the online one was equal to 61%, without losing specificity. The introduction of the prognostic factors Her2 and Ki67 could help improving performances on the classification of particularly type of patients. CONCLUSIONS Although the training of the model on the sample of T1 patients has brought a significant improvement in performance, the general performance does not yet allow a clinical application of the algorithm. However, the experimental results encourage future developments aimed at introducing features of a different nature in the CM model.
Collapse
Affiliation(s)
- Raffaella Massafra
- Department of Health Physics, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
37
|
Massafra R, Bove S, Lorusso V, Biafora A, Comes MC, Didonna V, Diotaiuti S, Fanizzi A, Nardone A, Nolasco A, Ressa CM, Tamborra P, Terenzio A, La Forgia D. Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images. Diagnostics (Basel) 2021; 11:diagnostics11040684. [PMID: 33920221 PMCID: PMC8070152 DOI: 10.3390/diagnostics11040684] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 02/06/2023] Open
Abstract
Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Samantha Bove
- Dipartimento di Matematica, Università degli Studi di Bari, 70121 Bari, Italy;
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.L.); (A.N.)
| | - Albino Biafora
- Dipartimento di Economia e Finanza, Università degli Studi di Bari, 70124 Bari, Italy;
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Sergio Diotaiuti
- Struttura Semplice Dipartimentale di Chirurgia, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
- Correspondence: ; Tel.: +39-080-555-5111
| | - Annalisa Nardone
- Unita Opertiva Complessa di Radioterapia, IRCCS Istituto Tumori ”Giovanni Paolo II”, 70124 Bari, Italy;
| | - Angelo Nolasco
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.L.); (A.N.)
| | - Cosmo Maurizio Ressa
- Unità Operativa Complessa di Chirurgica Plastica e Ricostruttiva, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (V.D.); (P.T.)
| | - Antonella Terenzio
- Unità di Oncologia Medica, Università Campus Bio-Medico, 00128 Roma, Italy;
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| |
Collapse
|
38
|
Massafra R, Latorre A, Fanizzi A, Bellotti R, Didonna V, Giotta F, La Forgia D, Nardone A, Pastena M, Ressa CM, Rinaldi L, Russo AOM, Tamborra P, Tangaro S, Zito A, Lorusso V. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Front Oncol 2021; 11:576007. [PMID: 33777733 PMCID: PMC7991309 DOI: 10.3389/fonc.2021.576007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 01/22/2021] [Indexed: 12/20/2022] Open
Abstract
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 with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Agnese Latorre
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi "Aldo Moro" e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Francesco Giotta
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Annalisa Nardone
- Unitá Opertiva Complessa di Radioterapia, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Maria Pastena
- Unitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Cosmo Maurizio Ressa
- Unitá Opertiva Complessa di Chirurgia Plastica e Ricostruttiva, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi "Aldo Moro" e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, Italy
| | - Alfredo Zito
- Unitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Vito Lorusso
- Unitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| |
Collapse
|
39
|
Massafra R, Pomarico D, La Forgia D, Bove S, Didonna V, Latorre A, Russo AO, Lorusso PTV, Fanizzi A. Decision support systems for the prediction of lymph node involvement in early breast cancer. J BUON 2021; 26:275-277. [PMID: 33721462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The prediction of lymph node involvement represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies. An artificial intelligence model able to predict it in pre-operative phase requires the interface among multiple data structures. The trade-off among time consuming, expensive and invasive methodologies is emerging in experimental setups exploited for the analysis of sentinel lymph nodes, where machine learning algorithms represent a key ingredient in recorded data elaboration. The accuracy required for clinical applications is obtainable matching different kind of data. Statistical associations of prognostic factors with symptoms and predictive models implemented also through on-line softwares represent useful diagnostic support tools which translate into patients quality of life improvement and costs reduction.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124 Bari, Italy
| | | | | | | | | | | | | | | | | |
Collapse
|
40
|
La Forgia D, Fanizzi A, Campobasso F, Bellotti R, Didonna V, Lorusso V, Moschetta M, Massafra R, Tamborra P, Tangaro S, Telegrafo M, Pastena MI, Zito A. Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome. Diagnostics (Basel) 2020; 10:E708. [PMID: 32957690 PMCID: PMC7555402 DOI: 10.3390/diagnostics10090708] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/07/2020] [Accepted: 09/16/2020] [Indexed: 02/07/2023] Open
Abstract
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 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER-, PR+/PR-, HER2+/HER2-, Ki67+/Ki67-, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2- (90.87%), ER+/ER- (83.79%) and Ki67+/Ki67- (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors' molecular subtype.
Collapse
Affiliation(s)
- Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Francesco Campobasso
- Dipartimento di Economia e Finanza, Università degli Studi di Bari “Aldo Moro”, Largo Abbazia S. Scolastica, 70124 Bari, Italy;
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari “Aldo Moro”, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Marco Moschetta
- Unità Operativa Semplice Dipartimentale Radiodiagnostica ad Indirizzo Senologico, Azienda Ospedaliero-Universitaria Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy; (M.M.); (M.T.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (A.F.); (V.D.); (P.T.)
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy;
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70121 Bari, Italy
| | - Michele Telegrafo
- Unità Operativa Semplice Dipartimentale Radiodiagnostica ad Indirizzo Senologico, Azienda Ospedaliero-Universitaria Consorziale Policlinico, Piazza Giulio Cesare 11, 70124 Bari, Italy; (M.M.); (M.T.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| |
Collapse
|
41
|
Fausto A, Fanizzi A, Volterrani L, Mazzei FG, Calabrese C, Casella D, Marcasciano M, Massafra R, La Forgia D, Mazzei MA. Feasibility, Image Quality and Clinical Evaluation of Contrast-Enhanced Breast MRI Performed in a Supine Position Compared to the Standard Prone Position. Cancers (Basel) 2020; 12:cancers12092364. [PMID: 32825583 PMCID: PMC7564182 DOI: 10.3390/cancers12092364] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/12/2020] [Accepted: 08/19/2020] [Indexed: 11/16/2022] Open
Abstract
Background: To assess the feasibility, image quality and diagnostic value of contrast-enhanced breast magnetic resonance imaging (MRI) performed in a supine compared to a prone position. Methods: One hundred and fifty-one patients who had undergone a breast MRI in both the standard prone and supine position were evaluated retrospectively. Two 1.5 T MR scanners were used with the same image resolution, sequences and contrast medium in all examinations. The image quality and the number and dimensions of lesions were assessed by two expert radiologists in an independent and randomized fashion. Two different classification systems were used. Histopathology was the standard of reference. Results: Two hundred and forty MRIs from 120 patients were compared. The analysis revealed 134 MRIs with monofocal (U), 68 with multifocal (M) and 38 with multicentric (C) lesions. There was no difference between the image quality and number of lesions in the prone and supine examinations. A significant difference in the lesion extension was observed between the prone and supine position. No significant differences emerged in the classification of the lesions detected in the prone compared to the supine position. Conclusions: It is possible to perform breast MRI in a supine position with the same image quality, resolution and diagnostic value as in a prone position. In the prone position, the lesion dimensions are overestimated with a higher wash-in peak than in the supine position.
Collapse
Affiliation(s)
- Alfonso Fausto
- Department of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy;
- Correspondence: ; Tel.: +39-0577585287 or +39-3477601341
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Luca Volterrani
- Department of Medical, Surgical and Neuro Sciences, Unit of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy; (L.V.); (M.A.M.)
| | - Francesco Giuseppe Mazzei
- Department of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy;
| | | | - Donato Casella
- Department of Oncologic and Reconstructive Breast Surgery, Azienda Ospedaliera Universitaria Senese, University Hospital of Siena, 53100 Siena, Italy;
| | - Marco Marcasciano
- Unità di Oncologia Chirurgica Ricostruttiva della Mammella, “Spedali Riuniti” di Livorno, Breast Unit Integrata di Livorno Cecina, Piombino Elba, Azienda USL Toscana Nord Ovest, 57100 Livorno, Italy;
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (A.F.); (R.M.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy;
| | - Maria Antonietta Mazzei
- Department of Medical, Surgical and Neuro Sciences, Unit of Diagnostic Imaging, University Hospital of Siena, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy; (L.V.); (M.A.M.)
| |
Collapse
|
42
|
La Forgia D, Fausto A, Gatta G, Di Grezia G, Faggian A, Fanizzi A, Cutrignelli D, Dentamaro R, Didonna V, Lorusso V, Massafra R, Tangaro S, Mazzei MA. Elite VABB 13G: A New Ultrasound-Guided Wireless Biopsy System for Breast Lesions. Technical Characteristics and Comparison with Respect to Traditional Core-Biopsy 14-16G Systems. Diagnostics (Basel) 2020; 10:diagnostics10050291. [PMID: 32397505 PMCID: PMC7277965 DOI: 10.3390/diagnostics10050291] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/01/2020] [Accepted: 05/05/2020] [Indexed: 02/07/2023] Open
Abstract
The typification of breast lumps with fine-needle biopsies is often affected by inconclusive results that extend diagnostic time. Many breast centers have progressively substituted cytology with micro-histology. The aim of this study is to assess the performance of a 13G-needle biopsy using cable-free vacuum-assisted breast biopsy (VABB) technology. Two of our operators carried out 200 micro-histological biopsies using the Elite 13G-needle VABB and 1314 14–16G-needle core biopsies (CBs) on BI-RADS 3, 4, and 5 lesions. Thirty-one of the procedures were repeated following CB, eighteen following cytological biopsy, and three after undergoing both procedures. The VABB Elite procedure showed high diagnostic performance with an accuracy of 94.00%, a sensitivity of 92.30%, and a specificity of 100%, while the diagnostic underestimation was 11.00%, all significantly comparable to of the CB procedure. The VABB Elite 13G system has been shown to be a simple, rapid, reliable, and well-tolerated biopsy procedure, without any significant complications and with a diagnostic performance comparable to traditional CB procedures. The histological class change in an extremely high number of samples would suggest the use of this procedure as a second-line biopsy for suspect cases or those with indeterminate cyto-histological results.
Collapse
Affiliation(s)
- Daniele La Forgia
- Radiodiagnostica Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (D.L.F.); (R.D.)
| | - Alfonso Fausto
- Dipartimento di Diagnostica per Immagini, Azienda Ospedaliera Universitaria Senese, Viale Bracci 10, 53100 Siena, Italy; (A.F.); (M.A.M.)
| | - Gianluca Gatta
- Dipartimento Medicina di Precisione, Università degli Studi della Campania Luigi Vanvitelli, Piazza L. Miraglia 2, 80138 Napoli, Italy;
| | - Graziella Di Grezia
- Dipartimento dei Servizi—Diagnostica per Immagini, Ospedale “G. Criscuoli”, Via Quadrivio, 83054 Avellino, Italy;
| | - Angela Faggian
- UOC Diagnostica per Immagini, Azienda Ospedaliera San Pio, Via dell’Angelo 1, 82100 Benevento, Italy;
| | - Annarita Fanizzi
- Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
- Correspondence: ; Tel.: +39-080-5555111
| | - Daniela Cutrignelli
- Chirurgia Plastica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Rosalba Dentamaro
- Radiodiagnostica Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (D.L.F.); (R.D.)
| | - Vittorio Didonna
- Fisica Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.D.); (R.M.)
| | - Vito Lorusso
- Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Raffaella Massafra
- Fisica Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (V.D.); (R.M.)
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari ‘Aldo Moro’, 70125 Bari, Italy;
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Giovanni Amendola, 165/a, 70126 Bari, Italy
| | - Maria Antonietta Mazzei
- Dipartimento di Diagnostica per Immagini, Azienda Ospedaliera Universitaria Senese, Viale Bracci 10, 53100 Siena, Italy; (A.F.); (M.A.M.)
| |
Collapse
|
43
|
Fanizzi A, Basile TMA, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, Didonna V, Fausto A, Massafra R, Moschetta M, Popescu O, Tamborra P, Tangaro S, La Forgia D. A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis. BMC Bioinformatics 2020; 21:91. [PMID: 32164532 PMCID: PMC7069158 DOI: 10.1186/s12859-020-3358-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.
Collapse
Affiliation(s)
- Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Teresa M A Basile
- Dip. Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", via G. Amendola 173, Bari, Italy.,INFN - Istituto Nazionale di Fisica Nucleare, sezione di Bari, via G. Amendola 173, Bari, Italy
| | - Liliana Losurdo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy.
| | - Roberto Bellotti
- Dip. Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", via G. Amendola 173, Bari, Italy.,INFN - Istituto Nazionale di Fisica Nucleare, sezione di Bari, via G. Amendola 173, Bari, Italy
| | - Ubaldo Bottigli
- Dip. di Scienze Fisiche, della Terra e dell'Ambiente, Università degli Studi di Siena, strada Laterina 2, Siena, Italy
| | - Rosalba Dentamaro
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Vittorio Didonna
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Alfonso Fausto
- Dip. di Diagnostica delle Immagini, Ospedale Universitario di Siena, viale Bracci 16, Siena, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Marco Moschetta
- Dip. Interdisciplinare di Medicina, Università degli Studi di Bari "A. Moro", piazza G. Cesare 11, Bari, Italy
| | - Ondina Popescu
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Pasquale Tamborra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| | - Sabina Tangaro
- INFN - Istituto Nazionale di Fisica Nucleare, sezione di Bari, via G. Amendola 173, Bari, Italy
| | - Daniele La Forgia
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy
| |
Collapse
|
44
|
La Forgia D, Catino A, Dentamaro R, Galetta D, Gatta G, Losurdo L, Massafra R, Scattone A, Tangaro S, Fanizzi A. Role of the contrast-enhanced spectral mammography for the diagnosis of breast metastases from extramammary neoplasms. J BUON 2019; 24:1360-1366. [PMID: 31646778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
PURPOSE Extramammary breast tumors are quite unusual but they might represent the first semiotic sign of non negative mammography. Thus, the need for an early and accurate diagnosis is crucial, with the purpose of planning and optimize the therapeuthical strategy and consequently to improve the clinical outcome of patients. METHODS Due to the intrinsic characteristics of this technique, CESM lends itself as a useful and reliable tool for a complex diagnosis, since it may simultaneously provide both the data of the mammographic semiotic and the dynamic one of an examination with a contrast medium. RESULTS In this article, the most common radiological signs of this type of lesions are summarized through an analysis of the published literature. The article focuses on the different mammographic semeiotics in primary and secondary malignant lesions in the breast, on the different aspects of metastases deriving from blood and lymphatic spread, as well as on the common analogies between metastatic lesions and fibroadenomas. Moreover, the characteristics of a unique case of breast metastasis from pleural mesothelioma, analyzed by Contrast-Enhanced Spectral Mammography, are described. CONCLUSIONS On the basis of our experience, CESM could represent an extremely valid method to address a correct diagnosis in complex cases of potentially metastatic lesions.
Collapse
|
45
|
Fanizzi A, Losurdo L, Basile TMA, Bellotti R, Bottigli U, Delogu P, Diacono D, Didonna V, Fausto A, Lombardi A, Lorusso V, Massafra R, Tangaro S, La Forgia D. Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images. J Clin Med 2019; 8:jcm8060891. [PMID: 31234363 PMCID: PMC6616937 DOI: 10.3390/jcm8060891] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/08/2019] [Accepted: 06/17/2019] [Indexed: 12/24/2022] Open
Abstract
Contrast-Enhanced Spectral Mammography (CESM) is a novelty instrumentation for diagnosing of breast cancer, but it can still be considered operator dependent. In this paper, we proposed a fully automatic system as a diagnostic support tool for the clinicians. For each Region Of Interest (ROI), a features set was extracted from low-energy and recombined images by using different techniques. A Random Forest classifier was trained on a selected subset of significant features by a sequential feature selection algorithm. The proposed Computer-Automated Diagnosis system is tested on 48 ROIs extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. The present method resulted highly performing in the prediction of benign/malignant ROIs with median values of sensitivity and specificity of 87.5% and 91.7%, respectively. The performance was high compared to the state-of-the-art, even with a moderate/marked level of parenchymal background. Our classification model outperformed the human reader, by increasing the specificity over 8%. Therefore, our system could represent a valid support tool for radiologists for interpreting CESM images, both reducing the false positive rate and limiting biopsies and surgeries.
Collapse
Affiliation(s)
- Annarita Fanizzi
- Dip. di Diagnosi e Terapia per Immagini, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| | - Liliana Losurdo
- Dip. di Diagnosi e Terapia per Immagini, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| | - Teresa Maria A Basile
- Dip. Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", 70125 Bari, Italy.
| | - Roberto Bellotti
- Dip. Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", 70125 Bari, Italy.
| | - Ubaldo Bottigli
- Dip. di Scienze Fisiche, della Terra e dell'Ambiente, Università degli Studi di Siena, 53100 Siena, Italy.
| | - Pasquale Delogu
- Dip. di Scienze Fisiche, della Terra e dell'Ambiente, Università degli Studi di Siena, 53100 Siena, Italy.
| | - Domenico Diacono
- INFN-Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
| | - Vittorio Didonna
- Dip. di Diagnosi e Terapia per Immagini, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| | - Alfonso Fausto
- Dip. di Diagnostica per Immagini, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy.
| | - Angela Lombardi
- INFN-Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
| | - Vito Lorusso
- Dip. Area Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| | - Raffaella Massafra
- Dip. di Diagnosi e Terapia per Immagini, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| | - Sabina Tangaro
- INFN-Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
| | - Daniele La Forgia
- Dip. di Diagnosi e Terapia per Immagini, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II" di Bari, 70124 Bari, Italy.
| |
Collapse
|
46
|
Basile TMA, Fanizzi A, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, Didonna V, Fausto A, Massafra R, Moschetta M, Tamborra P, Tangaro S, La Forgia D. Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system. Phys Med 2019; 64:1-9. [PMID: 31515007 DOI: 10.1016/j.ejmp.2019.05.022] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 05/08/2019] [Accepted: 05/25/2019] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.
Collapse
Affiliation(s)
- T M A Basile
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy.
| | - A Fanizzi
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - L Losurdo
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - R Bellotti
- Department of Physics, University of Bari "Aldo Moro", Bari, Italy; INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - U Bottigli
- Department of Physical Sciences, Earth and Environment, University of Siena, Siena, Italy
| | - R Dentamaro
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - V Didonna
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - A Fausto
- Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy
| | - R Massafra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - M Moschetta
- Interdisciplinary Department of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - P Tamborra
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| | - S Tangaro
- INFN National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - D La Forgia
- I.R.C.C.S. "Giovanni Paolo II" National Cancer Centre, Bari, Italy
| |
Collapse
|
47
|
Tamborra P, Martinucci E, Massafra R, Bettiol M, Capomolla C, Zagari A, Didonna V. The 3D isodose structure-based method for clinical dose distributions comparison in pretreatment patient-QA. Med Phys 2018; 46:426-436. [PMID: 30450559 DOI: 10.1002/mp.13297] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 11/07/2018] [Accepted: 11/08/2018] [Indexed: 11/05/2022] Open
Abstract
INTRODUCTION Before the approval of any Intensity Modulated Radiation Therapy or Volumetric Modulated Arc Therapy treatment plan, quality assurance (QA) tests are needed to reveal potential errors such as an inaccurate calculation of the dose distribution, the failure of the record-and-verify system, or the delivery system of the linear accelerator. Currently, the method adopted to compare the measured dose distribution with the treatment planning system TPS calculated dose distribution is gamma analysis. However, gamma analysis has been shown to be ineffective for the clinical evaluation of treatment plans. We proposed and tested a new method (the isodose structures method) alternative to gamma analysis. METHOD Different errors were introduced in 33 error-free Head and Neck plans. The modified plans were recalculated using TPS software and the dose distributions obtained were compared to those of the original (error-free) plans. The comparison was performed using gamma analysis and the new method. The target was to calculate overall and organ-specific gamma passing rates as well as the overlapping ratio (OR) and volume ratio (VR) factors of the isodose structures method for each error-included plan. RESULTS Eight of the 33 plans passed both the gamma analysis and the isodose structures (IS) analysis, ten plans did not pass either of them, while 13 plans which did not pass the IS analysis, passed the gamma analysis. Two plans which did not pass gamma, passed IS analysis. Furthermore, Dose Volume Histogram (DVH) metrics could not detect the low agreement between the dose distributions of two error-free plans and the respective modified plans. In this case, the IS analysis also allowed us to detect clinically meaningful differences between measured and TPS dose distributions. CONCLUSIONS The IS method analysis clearly showed a high efficiency in detecting clinically relevant differences between TPS and measured dose distributions not seen in gamma analysis and in DVH-based metrics. Therefore, IS analysis proved to be a valid tool, alternative to gamma analysis for dose comparison in patient-specific QA test.
Collapse
Affiliation(s)
- Pasquale Tamborra
- Department of Medical Physics, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, 70124, Italy
| | - Erica Martinucci
- Department of Medical Physics, Hospital "Vito Fazzi" - Cancer Centre "Giovanni Paolo II", Lecce, 70130, Italy
| | - Raffaella Massafra
- Department of Medical Physics, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, 70124, Italy
| | - Marco Bettiol
- Department of Medical Physics, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, 70124, Italy
| | - Caterina Capomolla
- Department of Medical Physics, Hospital "Vito Fazzi" - Cancer Centre "Giovanni Paolo II", Lecce, 70130, Italy
| | - Annarita Zagari
- Department of Medical Physics, Hospital "Vito Fazzi" - Cancer Centre "Giovanni Paolo II", Lecce, 70130, Italy
| | - Vittorio Didonna
- Department of Medical Physics, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, 70124, Italy
| |
Collapse
|
48
|
Massafra R, Nardone A, Tamborra P, Carbonara R, Lioce M, Pascali A, Didonna V. 198. Dosimetric comparison of 3D-Conformal Radiotherapy versus RapidArc for adjuvant treatment of advanced gastric cancer: IRCCS Giovanni Paolo II case study. Phys Med 2018. [DOI: 10.1016/j.ejmp.2018.04.209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
|
49
|
Tamborra P, Bettiol M, Carbonara R, Rito AD, Lioce M, Milella A, Nardone A, Necchia R, Didonna V, Massafra R. 212. RapidArc versus IMRT for postoperative irradiation of a case of recurrent breast cancer with internal mammary lymph node involvement. Phys Med 2018. [DOI: 10.1016/j.ejmp.2018.04.223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
|
50
|
Carbonara R, Bettiol M, Tamborra P, Bonaduce S, Cristofaro C, Lioce M, Nardone A, Scognamillo G, Didonna V, Massafra R. 208. Investigation on target motion with intraprostatic fiducial markers and daily CBCT in radical radiotherapy for prostate cancer. Phys Med 2018. [DOI: 10.1016/j.ejmp.2018.04.219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
|