diff --git a/MaskHIT_Prep/03_get_patches_meta.py b/MaskHIT_Prep/03_get_patches_meta.py
index 3699b94..085ad1b 100644
--- a/MaskHIT_Prep/03_get_patches_meta.py
+++ b/MaskHIT_Prep/03_get_patches_meta.py
@@ -108,6 +108,9 @@ def extract_tcga_info(path):
         # TODO: This should work but need to check with real data.
         df['id_patient'], df['type'] = zip(*df['file'].apply(extract_tcga_info))
     else:
+        #TODO: This behavior seems dangerous. Shouldn't we have a separated script
+        #and describe the input files definition so we don't need to update code
+        #based on a dataset?
         df['id_patient'] = df['id_svs']
         df['type'] = '01Z'
 
diff --git a/MaskHIT_Prep/04_feature_extraction.py b/MaskHIT_Prep/04_feature_extraction.py
index 00cbe86..da80965 100644
--- a/MaskHIT_Prep/04_feature_extraction.py
+++ b/MaskHIT_Prep/04_feature_extraction.py
@@ -100,6 +100,21 @@ def main():
 
     model = create_model(num_layers, True, 1)
     model.fc = nn.Identity()
+
+    # User-defined feature extractor
+    if config.patch.pretrained_model_path:
+        # Load the pretrained model state dict
+        pretrained_dict = torch.load(config.patch.pretrained_model_path)
+
+        if isinstance(pretrained_dict, nn.Module):
+            # If it's a full model, convert to a state dict from it
+            pretrained_dict = pretrained_dict.state_dict()
+
+        # Filter out `fc` layer from the pretrained_dict
+        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k not in ['fc.weight', 'fc.bias']}
+        # Load the filtered state dict into your model
+        model.load_state_dict(pretrained_dict, strict=False)
+
     model.cuda()
     model.eval()
 
diff --git a/MaskHIT_Prep/05_post_process.py b/MaskHIT_Prep/05_post_process.py
index 98d6452..89dbd9a 100644
--- a/MaskHIT_Prep/05_post_process.py
+++ b/MaskHIT_Prep/05_post_process.py
@@ -153,6 +153,7 @@ def get_counts_chunck(data, delta):
     df_svs = df_svs.merge(df_sum, on='id_svs', how='inner')
     df_svs = df_svs.loc[df_svs.valid > 25]
     df_svs.to_pickle(svs_meta)
+    df_svs.head(2).to_csv(str(svs_meta).replace('.pickle', '_preview.txt'), index=False, sep='\t')
 
     print("")
     print("=" * 40)
diff --git a/MaskHIT_Prep/configs/config_default.yaml b/MaskHIT_Prep/configs/config_default.yaml
index 92e32c4..c6b880c 100644
--- a/MaskHIT_Prep/configs/config_default.yaml
+++ b/MaskHIT_Prep/configs/config_default.yaml
@@ -10,6 +10,12 @@
 #  (custom): more likely to need modification for each user
 #---------------------------------------------------
 
+#TODO: Clarify which values are minimally required.
+#Suggestions:
+# <SET_YOUR_OWN_VALUE> for required values.
+# null for optional value. When use null, don't add typehint.
+# For null-able values, we should not add typehint.
+# Instead, note the type in the comment: (optional, string)
 
 study:
   ## Study Configuration
@@ -22,23 +28,29 @@ study:
   # (1)
   # Indicates a dataset class for json processing pipeline
   # See datasets/ directory for available classes.
-  # Available: TCGA, GramStains, IBD
-  # Please implement a dataset file if necessary
+  # Currently available: TCGA, GramStains, IBD, RCCp
+  # Please implement a dataset file if necessary.
   dataset_type: !!str <SET_YOUR_OWN_VALUE>
 
   # (0,1)
   # Path to the folder containing svs data
   # Example: /pool2/data/WSI_TCGA/Colorectal
   # Set to "tiles" if using tile preprocessing
+  # This can be left unset if you have a JAON file with each slide path
+  # and properly parsed in your dataset class.
   svs_dir: !!str <SET_YOUR_OWN_VALUE>
 
   # (1)
   # Path to the json file with dataset info.
+  # Please read README to prepare a JSON file.
   json_path: !!str <SET_YOUR_OWN_VALUE>
 
   # (1)
-  # Dataset field to use with stratification.
-  stratify_by: !!str status
+  # Dataset field to use with stratification (Optional[String]).
+  # Set null for no stratification.
+  #TODO: Change the default value from 'status'. It should be <SET_YOUR_OWN_VALUE> 
+  #and add examples?.
+  stratify_by: null
 
   # (1)
   # Number of folds for kfold cross validation
@@ -100,6 +112,7 @@ patch:
   # (2, 3, 5)
   # Path to the pickle file containing metadata for the .svs files
   # Example: meta/dhmc_rcc_svs.pickle
+  # TODO: This is a saving path, right? We should clarify that this is not input.
   svs_meta: !!str <SET_YOUR_OWN_VALUE>
 
   # (2, 4)
@@ -108,7 +121,17 @@ patch:
 
   # (4, 5)
   # Backbone model for feature extraction
-  backbone: resnet_18
+  # Currently, only models from the ResNet family are available.
+  # Default: resnet_18, using ImageNet features.
+  # Replace with another ResNet model if necessary.
+  backbone: !!str resnet_18
+
+  # (4, 5)
+  # Path to a custom feature extractor snapshot (optional, string)
+  # Optional: Set this only if you have a specialized feature extractor,
+  # possibly pretrained on a relevant dataset.
+  # Default: Uses the standard ImageNet pretrained model.
+  pretrained_model_path: null
 
   # (4)
   # Batch size for processing slide patches
@@ -122,6 +145,7 @@ patch:
   # (2)
   # Mask magnification for color filtering during patch extraction.
   # A lower magnification will run faster but will be less precise
+  #TODO: Ask why this value?
   mag_mask: !!float 0.3125
 
 feature: