Clinical trial’s workflow of the second phase.

Prior to the kick-off of the second phase, 15 participants in each centre, all having NF breasts, have been examined by MammoWave and data has been used to calibrate the image parameters’ thresholds. MammoWave images were reviewed by a central assessor (an independent external scientist), who had no access to the reference standard data; the central assessor discarded MammoWave outputs due to the presence of spurious peaks in MammoWave images. Subsequently, the microwave imaging output of MammoWave is compared to the output of the radiologist study review (from conventional exams).</p

Example of WF breast: Mammographic heterogeneously dense (ACR C) right breast of 50 years old woman with a palpable nodule of 10 mm.

Histopathology output is also given in the insert. Microwave images, normalized to unitary average of the intensity, are given in the top row for three different conductivity weightings (from left to right: 0.3 S/m, 0.4 S/m and 0.5 S/m, respectively). Microwave images are given here as 2D images in the azimuthal, i.e., coronal plane. Moreover, 1D intensity projection on X and Y is displayed in the inserts. X and Y are given in meters; intensity is in arbitrary units. All microwave images show a non-homogeneous behavior, with a main peak indicated by the red arrows. The proposed rule-of-thumb classifies this breast as positive (the values of the microwave images’ selected features are provided in the S1B Table).</p

S1 Appendix -

Microwave imaging is a safe and promising new technology in breast radiology, avoiding discomfort of breast compression and usage of ionizing radiation. This paper presents the first prospective microwave breast imaging study during which both symptomatic and asymptomatic subjects were recruited. Specifically, a prospective multicentre international clinical trial was performed in 2020–2021, to investigate the capability of a microwave imaging device (MammoWave) in allowing distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e., with benign or malignant lesions. Each breast scan was performed with the volunteers lying on a dedicated examination table in a comfortable prone position. MammoWave output was compared to reference standard (i.e., radiologic study obtained within the last month and integrated with histological one if available and deemed necessary by responsible investigator) to classify breasts into NF/WF categories. MammoWave output consists of a selection of microwave images’ features (determined prior to trials’ start), which allow distinction between NF and WF breasts (using statistical significance p</div

Example of WF breast: Mammographic low density (ACR B) left breast of 70 years old woman with a mammographic parenchymal distortion of 8 mm confirmed by DBT in the lower outer quadrant.

Histopathology output is also given in the insert. Microwave images, normalized to unitary average of the intensity, are given in the top row for three different conductivity weightings (from left to right: 0.3 S/m, 0.4 S/m and 0.5 S/m, respectively). Microwave images are given here as 2D images in the azimuthal, i.e., coronal plane. Moreover, 1D intensity projection on X and Y is displayed in the inserts. X and Y are given in meters; intensity is in arbitrary units. All microwave images show a non-homogeneous behavior, with a main peak indicated by the red arrows. The proposed rule-of-thumb classifies this breast as positive (the values of the microwave images’ selected features are provided in the S1B Table).</p

Fig 1 -

MammoWave system, sketch of the breast imaging configuration showing the cylindrical hub and the antennas (left). Transmitting and receiving antenna configuration, showing the five triplet sections (right).</p

Example of NF breast: 50 years old woman, mammographic low-density (ACR B), left breast (DBT and ultrasonography BI-RADS 1).

Microwave images, normalized to unitary average of the intensity, are given in the top row for three different conductivity weightings (from left to right: 0.3 S/m, 0.4 S/m and 0.5 S/m, respectively). Microwave images are given here as 2D images in the azimuthal, i.e., coronal plane. Moreover, 1D intensity projection on X and Y is displayed in the inserts. X and Y are given in meters; intensity is in arbitrary units. The proposed rule-of-thumb classifies this breast as negative (the values of the microwave images’ selected features are provided in the S1B Table).</p

Example of WF breast: Mammographic high-density (ACR D), left breast of 66 years old woman, with a group of microcalcifications.

Histopathology output is also given in the insert. Microwave images, normalized to unitary average of the intensity, are given in the top row for three different conductivity weightings (from left to right: 0.3 S/m, 0.4 S/m and 0.5 S/m, respectively). Microwave images are given here as 2D images in the azimuthal, i.e., coronal plane. Moreover, 1D intensity projection on X and Y is displayed in the inserts. X and Y are given in meters; intensity is in arbitrary units. All microwave images show a non-homogeneous behavior, with a main peak indicated by the red arrows. The proposed rule-of-thumb classifies this breast as positive (the values of the microwave images’ selected features are provided in the S1B Table).</p

Supporting information with patient and breast info (S1A Table) and methods/features (S1B Table).

Supporting information with patient and breast info (S1A Table) and methods/features (S1B Table).</p

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p p p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics</p