Quantitative imaging biomarker for lung cancer
View Patent ↗In one or more implementations, systems, methods and computer implemented processes are provided that are directed to a method of treating a subject with a lung tumor, the method comprising: obtaining computed tomography (CT) image slices of the subject, wherein the CT image slices comprise images of the lung tumor. In a further implementation, the systems, methods and computer implemented processes are directed to identifying a first CT image slice where the lung tumor has a largest diameter among the CT image slices; and determining intensity-skewness of the lung tumor on the first CT image slice. In a further implementation, the systems, methods and computer implemented processes are directed to treating the subject with surgery, chemotherapy and/or radiotherapy, if the intensity-skewness is no greater than −1.5.
1 . A method of treating a subject with a lung tumor, the method comprising:
(a) obtaining computed tomography (CT) image slices of the subject, wherein the CT image slices comprise images of the lung tumor;
(b) identifying a first CT image slice where the lung tumor has a largest diameter among the CT image slices;
(c) determining intensity-skewness of the lung tumor on the first CT image slice;
(d) classifying the subject to one of disease-free-survival (DFS)-associated histologic subgroup based on a comparison of the intensity-skewness to a threshold value; and
(e) treating the subject with at least one of surgery, chemotherapy and/or radiotherapy, where the intensity-skewness is no greater than the threshold value.
2 . The method of claim 1 , wherein the intensity-skewness is:
Intensity_Skewness
=
1
N
∑
i
=
1
N
(
X
(
i
)
-
X
¯
)
3
(
1
N
∑
i
=
1
N
(
X
(
i
)
-
X
¯
)
2
)
3
wherein the first CT image slice N pixels: 1, . . . ith, . . . N, X(i) being an intensity of the ith pixel within the first CT image slice, X being a mean intensity of all pixels within the first CT image slice.
3 . The method of claim 1 , wherein the threshold value is accessed from a pre-trained machine learning model configured to output a value that corresponds to the demarcation between a first disease-free-survival (DFS)-associated histologic subgroup and a second disease-free-survival (DFS)-associated histologic subgroup-, wherein the pre-trained model is trained using a training set of CT images.
4 . A method for treating a subject with a lung tumor, the method comprising:
(a) obtaining computed tomography (CT) image slices of the subject, wherein the CT image slices comprise images of the lung tumor;
(b) identifying a first CT image slice where the lung tumor has a largest diameter among the CT image slices;
(c) determining intensity-skewness of the lung tumor on the first CT image slice;
(d) determining that the lung tumor is a mid/poor disease-free-survival (DFS)-associated histologic subgroup, if the intensity-skewness is no greater than −1.5; and
(e) treating the subject with at least one of surgery, chemotherapy and/or radiotherapy.
5 . The method of claim 4 , wherein intensity-skewness is:
Intensity_Skewness
=
1
N
∑
i
=
1
N
(
X
(
i
)
-
X
¯
)
3
(
1
N
∑
i
=
1
N
(
X
(
i
)
-
X
¯
)
2
)
3
wherein the first CT image slice has N pixels: 1, . . . ith, . . . N, X(i) being an intensity of the ith pixel within the first CT image slice, X being a mean intensity of all pixels within the first CT image slice.
6 . The method of claim 1 , wherein the lung tumor is lung adenocarcinoma.
7 . The method of claim 6 , wherein the lung adenocarcinoma is invasive adenocarcinoma.
8 . The method of claim 7 , wherein the lung adenocarcinoma is acinar predominant (ACI) adenocarcinoma, papillary predominant (PAP) adenocarcinoma, micropapillary predominant (MIP) adenocarcinoma, and solid predominant (SOL) adenocarcinoma; or invasive mucinous (MUC) adenocarcinoma.
9 . The method of claim 1 , wherein the lung tumor is non-small cell lung cancer (NSCLC).
10 . The method of claim 1 , wherein the chemotherapy comprises a platinum-based antineoplastic drug.
11 . The method of claim 10 , wherein the platinum-based antineoplastic drug is cisplatin and/or or carboplatin.
12 . The method of claim 1 , wherein the chemotherapy comprises gemcitabine, paclitaxel, docetaxel, pemetrexed, etoposide, vinorelbine, topotecan, irinotecan, or combinations thereof.
13 . The method of claim 1 , wherein the surgery comprises a wedge resection.
14 . The method of claim 4 , wherein the lung tumor is lung adenocarcinoma.
15 . The method of claim 14 , wherein the lung adenocarcinoma is acinar predominant (ACI) adenocarcinoma, papillary predominant (PAP) adenocarcinoma, micropapillary predominant (MIP) adenocarcinoma, and solid predominant (SOL) adenocarcinoma; or invasive mucinous (MUC) adenocarcinoma.
16 . The method of claim 4 , wherein the lung tumor is non-small cell lung cancer (NSCLC).