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Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.
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Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a "machines-in-the-loop" approach.
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Inteligência Artificial , Humanos , RadiologiaRESUMO
If Europe's health systems make a conscious decision to increase their utilization of technology and techniques that can enhance prevention and expedite early-stage diagnosis, they can effectively address the growing challenges of disease. By embracing these advancements, these health systems can significantly improve their response to emerging health issues.However, at present the effective integration and exploitation of these opportunities remains hesitant and suboptimal, and health and health services underperform accordingly, with patients suffering from the continuing variations in diagnosis and access to innovation. This paper presents a comprehensive study that examines the current state of various influential disciplines and factors in European countries. It specifically focuses on the adoption of Next Generation Screening technologies and the development stage of Public Health Genomics. The assessment of these areas is presented in the context of a rapidly changing policy environment, which provides an opportunity for a fundamental reconsideration of how and where new tools can be integrated into healthcare systems and routine practices. Top of Form.
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Atenção à Saúde , Genômica , Sequenciamento de Nucleotídeos em Larga Escala , Saúde Pública , Humanos , Genômica/métodos , Saúde Pública/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodosRESUMO
In the landscape of cancer treatment, particularly in the realm of breast cancer management, effective communication emerges as a pivotal factor influencing patient outcomes. This article delves into the nuanced intricacies of communication skills, specifically spotlighting the strategies embraced by breast radiologists. By examining the ramifications of communication on patient experience, interdisciplinary collaboration, and legal ramifications, this study underscores the paramount importance of empathetic and comprehensive communication approaches. A special emphasis is placed on the utilization of the SPIKES protocol, a structured method for conveying sensitive health information, and the deployment of strategies for navigating challenging conversations. Furthermore, the work encompasses the significance of communication with caregivers, the integration of artificial intelligence, and the acknowledgement of patients' psychological needs. By adopting empathetic communication methodologies and fostering multidisciplinary collaboration, healthcare practitioners have the potential to enhance patient satisfaction, promote treatment adherence, and augment the overall outcomes within breast cancer diagnosis. This paper advocates for the implementation of guidelines pertaining to psychological support and the allocation of sufficient resources to ensure the provision of holistic and patient-centered cancer care. The article stresses the need for a holistic approach that addresses patients' emotional and psychological well-being alongside medical treatment. Through thoughtful and empathetic communication practices, healthcare providers can profoundly impact patient experiences and breast cancer journeys in a positive manner.
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Invasive lobular cancer (ILC) is the most common of the breast cancer special types, accounting for up to 15% of all breast malignancies. The distinctive biological features of ILC include the loss of the cell adhesion molecule E-cadherin, which drives the tumor's peculiar discohesive growth pattern, with cells arranged in single file and dispersed throughout the stroma. Typically, such tumors originate in the lobules, are more commonly bilateral compared to invasive ductal cancer (IDC) and require a more accurate diagnostic examination through imaging. They are luminal in molecular subtype, and exhibit estrogen and progesterone receptor positivity and HER2 negativity, thus presenting a more unpredictable response to neoadjuvant therapies. There has been a significant increase in research focused on this distinctive breast cancer subtype, including studies on its pathology, its clinical and surgical management, and the high-resolution definition of its genomic profile, as well as the development of new therapeutic perspectives. This review will summarize the heterogeneous pattern of this unique disease, focusing on challenges in its comprehensive clinical management and on future insights and research objectives.
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Neoplasias da Mama , Carcinoma Lobular , Humanos , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/genética , Neoplasias da Mama/terapia , Neoplasias da Mama/patologia , Neoplasias da Mama/prevenção & controle , Feminino , Carcinoma Lobular/diagnóstico , Carcinoma Lobular/terapia , Carcinoma Lobular/genética , Carcinoma Lobular/patologiaRESUMO
Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.
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Importance: Pathogenic or likely pathogenic (P/LP) germline CDH1 variants are associated with risk for diffuse gastric cancer and lobular breast cancer (LBC) in the so-called hereditary diffuse gastric cancer (HDGC) syndrome. However, in some circumstances, LBC can be the first manifestation of this syndrome in the absence of diffuse gastric cancer manifestation. Objectives: To evaluate the frequency of germline CDH1 variants in women with the hereditary LBC (HLBC) phenotype, somatic CDH1 gene inactivation in germline CDH1 variant carriers' tumor samples, and the association of genetic profiles with clinical-pathological data and survival. Design, Setting, and Participants: This single-center, longitudinal, prospective cohort study was conducted from January 1, 1997, to December 31, 2021, with follow-up until January 31, 2023. Women with LBC seen at the European Institute of Oncology were included. Testing for germline CDH1, BRCA1, and BRCA2 genes was performed. Somatic profiling was assessed for germline CDH1 carriers. Main Outcomes and Measures: Accurate estimates of prevalence of germline CDH1 variants among patients with HLBC and the association of somatic sequence alteration with HLBC syndrome. The Kaplan-Meier method and a multivariable Cox proportional hazards regression model were applied for overall and disease-free survival analysis. Results: Of 5429 cases of primary LBC, familial LBC phenotype accounted for 1867 (34.4%). A total of 394 women with LBC were tested, among whom 15 germline CDH1 variants in 15 unrelated families were identified. Among these variants, 6 (40.0%) were P/LP, with an overall frequency of 1.5% (6 of 394). Of the 6 probands with P/LP CDH1 LBC, 5 (83.3%) had a positive family history of BC and only 1 (16.7%) had sporadic juvenile early-onset LBC. No germline BRCA1 and BRCA2 variants were identified in CDH1 carriers. An inactivating CDH1 mechanism (second hit) was identified in 4 of 6 explored matched tumor samples (66.7%) in P/LP germline carriers. The P/LP CDH1 LBC variant carriers had a significantly lower age at diagnosis compared with the group carrying CDH1 variants of unknown significance or likely benign (42.5 [IQR, 38.3-43.0] vs 51.0 [IQR, 45.0-53.0] years; P = .03). Conclusions and Relevance: In this cohort study, P/LP germline CDH1 variants were identified in individuals not fulfilling the classic clinical criteria for HDGC screening, suggesting that identification of these variants may provide a novel method to test women with LBC with early age at diagnosis and/or positive family history of BC.
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Antígenos CD , Neoplasias da Mama , Caderinas , Mutação em Linhagem Germinativa , Fenótipo , Humanos , Feminino , Neoplasias da Mama/genética , Pessoa de Meia-Idade , Caderinas/genética , Antígenos CD/genética , Estudos Prospectivos , Adulto , Predisposição Genética para Doença , Carcinoma Lobular/genética , Carcinoma Lobular/patologia , Estudos Longitudinais , Genótipo , IdosoRESUMO
BACKGROUND: Breast implants are not lifelong, with implant rupture being the third leading cause of revisional surgery in augmented women. Noncontrast MRI is a reliable tool to assess implant integrity; however, false positive and false negative diagnoses have been reported due to an incorrect interpretation of MRI signs. This study aims to investigate the incidence of these misleading results, comparing MRI findings with intraoperative surgical observations and exploring signs of nonunivocal interpretation. MATERIALS AND METHODS: Between March 2019 and October 2022, our hospital, a referral center for breast cancer care, conducted 139 breast MRI examinations to evaluate implant integrity. Surgical intervention was deemed necessary for patients diagnosed with suspected or confirmed implant rupture at MRI. Those patients who did not undergo any surgical procedure (63 cases) or had surgery at different institutes (11 cases) were excluded. RESULTS: Among the 65 patients who underwent preoperative MRI and subsequent surgery at our institute, surgical findings confirmed the preoperative MRI diagnosis in 48 women. Notably, 17 women exhibited a discordance between MRI and surgical findings: three false negatives, 11 false positives and three possible ruptures not confirmed. Signs of nonunivocal or misleading interpretation were assessed on a patient-by-patient basis. The importance of obtaining detailed information about a patient's breast implant, including fill materials, number of lumens, manufacturer and shape, proved immensely beneficial for interpreting MRI signs accurately. CONCLUSION: Pre-MRI knowledge of implant details and a meticulous evaluation of non-univocal signs can aid radiologists in accurately assessing implant integrity, reducing the risk of unnecessary revisional surgeries, and potentially averting allegations of medical malpractice.
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Background: Abemaciclib is currently approved for the adjuvant treatment of high-risk, lymph node (LN)-positive, hormone receptor (HR)-positive breast cancer (BC). In a real-world setting the clinicopathologic features of patients potentially eligible for adjuvant abemaciclib remain to be defined. There are conflicting data regarding the biological behavior and long-term outcomes across invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC). In our study we retrospectively assessed the real-world data and long-term outcome of selected high-risk features ILC compared to IDC, according to the MonarchE trial inclusion criteria. Methods: We identified 15,071 patients who got surgery at the European Institute of Oncology for a first primary, non-metastatic, HR-positive, HER2-negative BC from 2000 to 2008. 11,981 (79.5%) patients had an IDC and 1524 (10.1%) an ILC. The remaining 1566 patients (10.4%) had either combined ductal and lobular breast cancer or another histological breast cancer subtype. According to the eligibility criteria of the MonarchE study, we identified two high-risk groups, based on high number of positive lymph nodes, large tumor size, or a high cellular proliferation as measured by tumor grade or biomarkers. Patients were matched by propensity score. Findings: A total of 2872 (21.3%) patients were selected as clinically high-risk, including 361/1524 ILC (23.7%) and 2511/11,981 IDC (21%). 322 high-risk ILC were matched with similar high-risk IDC. The median follow-up was 13.2 years for survival. In the matched set, invasive disease-free survival (IDFS) (log-rank P = 0.09) and overall survival (OS) (log-rank P = 0.48) were not statistically significantly different between the two histological groups. For IDC patients, the 5-year and 10-year IDFS rates (95% CI) were 77.7% (72.9-82.2) and 57.3% (51.7-63.1) respectively, compared to the 5-year and 10-year IDFS rates of ILC patients that were 75.5% (70.6-80.2) and 50.7% (45.0-56.6). The 5-year and 10-year distant relapse free survival (DRFS) rates were 80% (75.3-84.2) and 65.3% (59.8-70.7) in IDC cohort, compared to the 5-year and the 10-year DRFS rates of 78.7% (74.0-83.1) and 61.5% (55.9-67.1) in the ILC cohort. Such data match the recent outcomes efficacy results of the MonarchE control arm. More patients in the ILC (n = 17) than in the IDC group (n = 10) developed axillary recurrence. At multivariable analysis, stratified for specific clinical features, age <35 years, pT2-3, axillary involvement with more than 10 positive axillary nodes were found to be predictors of unfavorable IDFS and OS in the overall matched high-risk population. Interpretation: Findings from this matched cohort study reported similar IDFS and DRFS rates for high risk HR positive early BC when compared to the control arm overall IDFS and DRFS rates reported from the MonarchE trial. Our study demonstrated rates of concordant long-term outcome status beyond histologic subtype. These data support an escalation strategy for these two different histological entities when diagnosed with high-risk features. In our dataset approximately 21% rate of high-risk HR positive early BC patients are potentially eligible for adjuvant abemaciclib treatment. Funding: Umberto Veronesi Foundation.
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Contrast-enhanced mammography (CEM) is a relatively recent diagnostic technique increasingly being utilized in clinical practice. Until recently, there was a lack of standardized reporting for CEM findings. However, this has changed with the publication of a supplement in the Breast Imaging Reporting and Data System (BI-RADS). A comprehensive understanding of CEM is essential for further enhancing its role in both screening and managing patients with breast malignancies. CEM can also be beneficial for problem-solving, improving the management of uncertain breast findings. Practitioners in this field should become more cognizant of how and when to employ this technique and interpret the various CEM findings. This paper aims to outline the key findings in the updated version of the BI-RADS specifically dedicated to CEM. Additionally, it will present some clinical cases commonly encountered in clinical practice.Critical relevance statement Standardized reporting and a thorough understanding of CEM findings are pivotal for advancing the role of CEM in screening and managing breast cancer patients. This standardization contributes significantly to integrating CEM as an essential component of daily clinical practice.Key points ⢠A complete knowledge and understanding of the findings outlined in the new BI-RADS CEM are necessary for accurate reporting.⢠BI-RADS CEM supplement is intuitive and practical to use.⢠Standardization of the CEM findings enables more accurate patient management.
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This retrospective study investigates the histopathological outcomes, upgrade rates, and disease-free survival (DFS) of high-risk breast lesions, including atypical ductal hyperplasia (ADH or DIN1b) and lobular in situ neoplasms (LIN), following Vacuum-Assisted Breast Biopsy (VABB) and surgical excision. The study addresses the challenge posed by these lesions due to their association with synchronous or adjacent Breast Cancer (BC) and increased future BC risk. The research, comprising 320 patients who underwent stereotactic VABB, focuses on 246 individuals with a diagnosis of ADH (120) or LIN (126) observed at follow-up. Pathological assessments, categorized by the UK B-coding system, were conducted, and biopsy samples were compared with corresponding excision specimens to determine upgrade rates for in situ or invasive carcinoma. Surgical excision was consistently performed for diagnosed ADH or LIN. Finally, patient follow-ups were assessed and compared between LIN and ADH groups to identify recurrence signs, defined as histologically confirmed breast lesions on either the same or opposite side. The results reveal that 176 (71.5%) patients showed no upgrade post-surgery, with ADH exhibiting a higher upgrade rate to in situ pathology than LIN1 (Atypical Lobular Hyperplasia, ALH)/LIN2 (Low-Grade Lobular in situ Carcinoma, LCIS) (38% vs. 20%, respectively, p-value = 0.002). Considering only patients without upgrade, DFS at 10 years was 77%, 64%, and 72% for ADH, LIN1, and LIN2 patients, respectively (p-value = 0.92). The study underscores the importance of a multidisciplinary approach, recognizing the evolving role of VABB. It emphasizes the need for careful follow-up, particularly for lobular lesions, offering valuable insights for clinicians navigating the complex landscape of high-risk breast lesions. The findings advocate for heightened awareness and vigilance in managing these lesions, contributing to the ongoing refinement of clinical strategies in BC care.
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The primary aim of our study was to assess the main mammographic and ultrasonographic features of invasive male breast malignancies. The secondary aim was to evaluate whether a specific radiological presentation would be associated with a worse receptor profile. Radiological images (mammography and/or ultrasound) of all patients who underwent surgery for male invasive breast cancer in our institution between 2008 and 2023 were retrospectively analyzed by two breast radiologists in consensus. All significant features of radiological presentation known in the literature were re-evaluated. Fifty-six patients were selected. The mean age at surgery of patients was 69 years (range: 35-81); in 82% of cases (46 patients), the histologic outcome was invasive ductal carcinoma. A total of 28 out of 56 (50%) patients had preoperative mammography; in 9/28 cases (32%), we found a mass with microcalcifications on mammography. The mass presented high density in 25 out of 28 patients (89%); the mass showed irregular margins in 15/28 (54%) cases. A total of 46 out of 56 patients had preoperative ultrasounds. The lesion showed a solid mass in 41/46 (89%) cases. In 5/46 patients (11%), the lesion was a mass with a mixed (partly liquid-partly solid) structure. We did not find any statistically significant correlation between major types of radiological presentation and tumor receptor arrangement. Knowledge of the main radiologic presentation patterns of malignant male breast neoplasm can help better manage this type of disease, which is rare but whose incidence is increasing.
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Breast cancer remains a significant global health challenge, with projections indicating a troubling increase in incidence. Breast cancer screening programs have long been hailed as life-saving initiatives, yet their true impact on mortality rates is a subject of ongoing debate. Screening poses the risk of false positives and the detection of indolent tumors, potentially leading to overtreatment. Bias factors, including lead time, length time, and selection biases, further complicate the assessment of screening efficacy. Recent studies suggest that AI-driven image analysis may revolutionize breast cancer screening, maintaining diagnostic accuracy while reducing radiologists' workload. However, the generalizability of these findings to diverse populations is a critical consideration. Personalized screening approaches and equitable access to advanced technologies are essential to mitigate disparities. In conclusion, the breast cancer screening landscape is evolving, emphasizing the need for risk stratification, appropriate imaging modalities, and a personalized approach to reduce overdiagnosis and focus on cancers with the potential to impact lives while prioritizing patient-centered care.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/epidemiologia , Detecção Precoce de Câncer/métodos , Radiologistas , Incidência , Mamografia/métodos , Programas de Rastreamento/métodosRESUMO
PURPOSE: To compare the diagnostic performance (detection, assessment of correct disease extent and multifocality/centricity) of Contrast-Enhanced Mammography (CEM) Versus Breast Magnetic Resonance (MRI) in the study of lobular neoplasms. METHODS: We retrospectively selected all the patients who underwent surgery for a lobular breast neoplasm, either an in situ or an invasive tumor, and had undergone both breast CEM and MRI examinations during the pre-surgical planning. Wilcoxon Signed Rank test was performed to assess the differences between size measurements using the different methods and the post-surgical pathological measurements, considered the gold standard. The agreement in identifying multifocality/multicentricity among the different methods and the pathology was assessed using the Kappa statistics. RESULTS: We selected 19 patients, of which one presented a bilateral neoplasm. Then, the images of these 19 patients were analyzed, for a total of 52 malignant breast lesions. We found no significant differences between the post-surgical pathological size of the lesions and the calculated size with CEM and MRI (p-value of the difference respectively 0.71 and 0.47). In all 20 cases, neoplasm detection was possible both with CEM and MRI. CEM and MRI showed an excellent ability to identify multifocal and multicentric cases (K statistic equal to 0.93 for both the procedures), while K statistic was 0.11 and 0.59 for FFDM and US, respectively. CONCLUSION: The findings of this study suggest that CEM is a reliable imaging technique in the preoperative setting of patients with lobular neoplasm, with comparable results to breast MRI.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/cirurgia , Estudos Retrospectivos , Meios de Contraste , Mamografia/métodos , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética , Sensibilidade e EspecificidadeRESUMO
Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.
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Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/genética , Inteligência Artificial , Mama/diagnóstico por imagem , Mamografia/métodos , Medição de Risco , Fatores de Risco , Detecção Precoce de Câncer/métodosRESUMO
BACKGROUND: Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications. METHODS: Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset). RESULTS: The dataset included 1,000 patients aged 21-73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively. CONCLUSIONS: The developed AI models accurately detect and characterize microcalcifications on mammography. RELEVANCE STATEMENT: AI-based systems have the potential to assist radiologists in interpreting microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening. KEY POINTS: ⢠A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications. ⢠Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms. ⢠The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.
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Neoplasias da Mama , Calcinose , Aprendizado Profundo , Humanos , Feminino , Inteligência Artificial , Estudos Retrospectivos , MamografiaRESUMO
This collection of 18 articles, comprising 12 original studies, 1 systematic review, and 5 reviews, is a collaborative effort by distinguished experts in breast cancer research, and it has been edited by Dr [...].
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Mammary Paget disease (MPD) is a rare condition primarily affecting adult women, characterized by unilateral skin changes in the nipple-areolar complex (NAC) and frequently associated with underlying breast carcinoma. Histologically, MPD is identified by large intraepidermal epithelial cells (Paget cells) with distinct characteristics. Immunohistochemical profiles aid in distinguishing MPD from other skin conditions. Clinical evaluation and imaging techniques, including magnetic resonance imaging (MRI), are recommended if MPD is suspected, although definitive diagnosis always requires histological examination. This review delves into the historical context, epidemiology, pathogenesis, clinical manifestations, and diagnosis of MPD, emphasizing the need for early detection. The classification of MPD based on pathogenesis is explored, shedding light on its varied presentations. Treatment options, including mastectomy and breast-conserving surgery, are discussed with clear guidelines for different scenarios. Adjuvant therapies are considered, particularly in cases with underlying breast cancer. Prognostic factors are outlined, underlining the importance of early intervention. Looking to the future, emerging techniques, like liquid biopsy, new immunohistochemical and molecular markers, and artificial intelligence-based image analysis, hold the potential to transform MPD diagnosis and treatment. These innovations offer hope for early detection and improved patient care, though validation through large-scale clinical trials is needed.