Impact of the COVID-19 lockdown in France on the diagnosis and staging of breast cancers in a tertiary cancer centre.

OBJECTIVES: Due to COVID-19, a lockdown took place between March 17 and May 1, 2020, in France. This study evaluates the impact of the lockdown on the diagnosis and staging of breast cancers in a tertiary cancer centre. METHODS: Our database was searched for all consecutive invasive breast cancers diagnosed in our institution during the lockdown (36 working days), during equivalent periods of 36 working days before and after lockdown and a reference period in 2019. The number and staging of breast cancers diagnosed during and after lockdown were compared to the pre-lockdown and reference periods. Tumour maximum diameters were compared using the Mann-Whitney test. Proportions of tumour size categories (T), ipsilateral axillary lymph node invasion (N) and presence of distant metastasis (M) were compared using Fisher's exact test. RESULTS: Compared to the reference period (n = 40 in average), the number of breast cancers diagnosed during lockdown (n = 32) decreased by 20% but increased by 48% after the lockdown (n = 59). After the lockdown, comparatively to the reference period, breast cancers were more often symptomatic (86% vs 57%; p = 0.001) and demonstrated bigger tumour sizes (p = 0.0008), the rates of small tumours (T1) were reduced by 38%, locally advanced cancers (T3, T4) increased by 80% and lymph node invasion increased by 64%. CONCLUSION: The COVID-19 lockdown was associated with a 20% decrease in the number of diagnosed breast cancers. Because of delayed diagnosis, breast cancers detected after the lockdown had poorer prognosis with bigger tumour sizes and higher rates of node invasion. KEY POINTS: • The number of breast cancer diagnosed in a large tertiary cancer centre in France decreased by 20% during the first COVID-19 lockdown. • Because of delayed diagnosis, breast cancers demonstrated bigger tumour size and more frequent axillary lymph node invasion after the lockdown. • In case of a new lockdown, breast screening programme and follow-up examinations should not be suspended and patients with clinical symptoms should be encouraged to seek attention promptly.

A case series of percutaneous transforamen ovale biopsies in cavernous sinus lesions.

BACKGROUND: The majority of cavernous sinus lesions are meningiomas, for which treatment (fractioned radiotherapy or radiosurgery), if indicated, is usually initiated upon image-based diagnosis. However, this region can be affected by a wide variety of pathological processes and the risk of misdiagnosis exists. As pathological diagnosis can be obtained by biopsy through the foramen ovale in selected cases, we asked the question as to whether systematically performing this procedure before treatment would provide additional, relevant diagnostic information. METHODS: All the cases referred to our department between January 2008 and December 2019 for cavernous sinus lesions that were considered for treatment and anatomically suitable for transforamen ovale biopsy were included. Outcomes and subsequent treatment or follow-up data were collected. RESULTS: Thirty-five patients were included. Twenty-six were highly suspected to have meningioma or schwannoma at imaging, among whom biopsy allowed diagnosis confirmation in 17 cases (65%). For the nine patients for whom biopsy was indicated upon suspected malignancy or inflammatory disease on imaging, biopsy revealed three meningiomas and one lymphoma and was not contributory in five cases (56%), three of which underwent open surgery. Three patients (8.5%) had persistent neuralgia at the last follow-up. CONCLUSIONS: When cavernous sinus meningioma or schwannoma is highly suspected upon predefined imaging criteria by an experienced neuroradiologist, invasive exploration before treatment does not seem to be indicated. Otherwise, transforamen ovale biopsy might be consider in selected cases as a minimally invasive option to obtain pathological analysis.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75).

BACKGROUND: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. PURPOSE: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. MATERIALS AND METHODS: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. RESULTS: A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. CONCLUSION: Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.