Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study

Figures & data

Table 1. Patients’ characteristics.

	N. (%)
M/F	32/21
Median age (range)	66.8 y (36–87)
1st line
Intermediate/poor prognosis	20 (100)
2nd−3rd line
Good prognosis	12 (36.4)
Intermediate/poor prognosis	21 (63.6)
Metastatic sites
lung	53 (100)
liver	15 (28.3)
lymph-nodes	28 (52.8)
bone	25 (47.2)
other (thyroid, parotid, soft tissue)	9 (17)

Figure 1. Progression free survival of patients in first (a) and second/third line setting (b).

Figure 2. Survival of patients with clinical benefit (solid line) or progression (dashed line). CR: complete response, PR: partial response; SD: stable disease; PD: progression of disease.

Figure 3. Survival of patients with clinical benefit (solid line) or progression (dashed line) in first (A) and second/third line setting (B). CR: complete response, PR: partial response; SD: stable disease; PD: progression of disease.

Table 2. Response rate.

	N. of patients	RR (%)	CB (%)
First line
Complete response	2	}50	}80
Partial response	8
Stable disease	6
Progression	4
Second/third line
Complete response	0	}24.2	}57.6
Partial response	8
Stable disease	11
Progression	14

RR: response rate (complete response + partial response).

CB: clinical benefit (complete response + partial response + stable disease).

Figure 4. a–b: the boxplots represent the distribution of F_stat_var and F_stat.Max between the two cohorts (DC: patients with disease control; PD: patients with progression of disease). c: Pearson correlation coefficients between the most significant features. d: the distribution of the cases (patients with disease control in blue, patients with progression of disease in red) in a space built with F_stat.Var on the x-axis and F_stat.Max on the y-axis.