Urological Melanoma: A Comprehensive Review of a Rare Subclass of Mucosal Melanoma with Emphasis on Differential Diagnosis and Therapeutic Approaches

Cazzato, Gerardo; Colagrande, Anna; Cimmino, Antonietta; Caporusso, Concetta; Candance, Pragnell Mary Victoria; Trabucco, S.; Zingarelli, Marcello; Lorusso, A; Marrone, Maricla; Stellacci, Alessandra; Arezzo, Francesca; Marzullo, Andrea; Serio, Gabriella; Filoni, Angela; Bonamonte, Domenico; Romita, Paolo; Foti, Caterina; Lettini, Teresa; Loizzi, Vera; Cormio, Gennaro; Resta, Leonardo; Rossi, Roberta; Ingravallo, Giuseppe

doi:10.3390/cancers13174424

Cited by 14 publications

(11 citation statements)

References 99 publications

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“…KIT is frequently mutated in VM, such as other urogenital melanomas, regardless of the employed technology [ 25 , 31 , 32 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. Exon 11 L576P KIT mutation has been frequently detected [ 31 , 32 , 35 , 36 , 40 , 41 , 43 , 46 ], suggesting Imatinib or Sunitinib as a possible use [ 31 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

Vulvar Malignant Melanoma: A Narrative Review

Falcicchio

Vinci

Cicinelli

et al. 2022

Cancers

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Malignant melanoma is a fatal disease that affects all skin sites. Among these, vulvar melanoma (VM) is a rare gynecological condition that accounts for 5% of all vulvar neoplasms. VM primarily affects older Caucasian women and its relationship to sun exposure is undefined. Diagnosis is defined by biopsy but many clinical, dermatoscopic, and confocal microscopic features can guide doctors. The molecular profile is characterized by the KIT mutation, revealed by all of the technologies that are used (classical sequencing, next-generation sequencing, and immunohistochemical staining). BRAF and NRAS mutations are also common in VM. All of these mutations are possible therapeutic targets. Today, surgery remains the first treatment choice for primary VM. The role of neoadjuvant and adjuvant therapy is scarce and the treatment of relapses is widely debated.

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Section: Resultsmentioning

confidence: 99%

Vulvar Malignant Melanoma: A Narrative Review

Falcicchio

Vinci

Cicinelli

et al. 2022

Cancers

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“…The rapid growth of a myometrial lesion may be suspected. However, the meaning of "rapid growth" is not well defined [13,14]. Additionally, studies have shown that even benign lesions can increase their size by 18% to 120% per year [15].…”

Section: Discussionmentioning

confidence: 99%

Overlap of Suspicious and Non-Suspicious Features in the Ultrasound Evaluations of Leiomyosarcoma: A Single-Center Experience

et al. 2023

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Leiomyosarcoma (LMS) is a rare type of mesenchymal tumor. Suspecting LMS before surgery is crucial for proper patient management. Ultrasound is the primary method for assessing myometrial lesions. The overlapping of clinical, laboratory, as well as ultrasound features between fibroids and LMS makes differential diagnosis difficult. We report our single-center experience in ultrasound imaging assessment of LMS patients, highlighting that misleading findings such as shadowing and absent or minimal vascularization may also occur in LMS. To avoid mistakes, a comprehensive evaluation of potentially overlapping ultrasound features is necessary in preoperative ultrasound evaluations of all myometrial tumors.

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“…Platinum-based NACT followed by radical hysterectomy has been suggested as an alternative approach to radiotherapy or CCRT in LACC, particularly for those with squamous cell histology, with objective response rates varying from 69.4-90.2%, pathological optimal response rates from 21.3-48.3%, 5-year disease-free survival (DFS) rates ranging from 55.4-71% and 5-year OS rates ranging from 58.9-81%, respectively (47)(48)(49)(50).…”

Section: Discussionmentioning

confidence: 99%

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

Arezzo

Cormio

Mongelli

et al. 2022

Arch Gynecol Obstet

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Purpose: Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show con icting data.Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients.Methods: We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with 10-fold cross-validation. The performances of the algorithm were assessed.Results: Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pretreatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre and post-treatment MRI, presence/absence of fornix in ltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79). Conclusions:We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.Authors contributions: Francesca Arezzo and Vera Loizzi performed the study concepyion. Gerardo Cazzato and Claudio Lombardi contributed to the study design. Material preparation and data collection were performed by Vincenzo Venerito, Ambrogio Cazzola and Michele Mongelli.

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Urological Melanoma: A Comprehensive Review of a Rare Subclass of Mucosal Melanoma with Emphasis on Differential Diagnosis and Therapeutic Approaches

Cited by 14 publications

References 99 publications

Vulvar Malignant Melanoma: A Narrative Review

Vulvar Malignant Melanoma: A Narrative Review

Overlap of Suspicious and Non-Suspicious Features in the Ultrasound Evaluations of Leiomyosarcoma: A Single-Center Experience

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

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